Neural Field Representations of Mobile Computational Photography

Ilya Chugunov

REVIEW 3 major objections 5 minor 226 references

Neural Field Representations of Mobile Computational Photography

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A carefully structured neural field, fit at test time to raw smartphone burst captures, can recover accurate depth, separate image layers, and stitch panoramas without labeled data, pre-processing, or learned priors.

desk verdict A well-written compilation of three strong peer-reviewed papers, but not new research and the abstract overclaims what the body honestly concedes. read the letter →

arxiv 2508.05907 v1 pith:E2AVE4YU submitted 2025-08-08 cs.CV

Ilya Chugunov This is my paper

classification cs.CV

keywords neuralfieldscomputationalphotographydepthestimationburstlayerseparationimagestitchingviewsynthesistest-timeoptimization

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis tries to establish that neural field models—small networks mapping coordinates to scene signals—can serve as a universal test-time representation for mobile computational photography. It shows that fitting such models directly to raw, unstabilized phone captures can recover dense depth from the tiny parallax of ordinary hand tremor, separate a scene into transmitted and obstructed layers, and stitch wide panoramas with parallax and lighting effects. The payoff, if correct, is that many inverse problems in photography can be solved by self-regularized fitting rather than by complex pipelines, ground-truth supervision, or large pre-trained models. The thesis demonstrates this across three chapters, each with its own neural field architecture and applications.

What carries the argument

The central machinery is the neural field itself, parameterized with multiresolution hash encodings for fast training and controllable spatial frequency. Depth uses a forward-projection RGB-D model with a learned planar background plus offset; layer separation uses backward-projected rays through two alpha-composited planes, with flows given by neural spline fields whose temporal smoothness is built into a cubic Hermite spline rather than regularized; image stitching relies on a ray-sphere intersection model with a view-dependent ray-offset network and a view-dependent color network. In each case the model's structure—plane regularization, spline parameterization, or a two-stage training sch

What would settle it

Capture a long-burst of a scene whose background is a close, curved, non-planar surface with no dominant plane. If the fitted depth collapses to a plane or follows image texture instead of true geometry—the paper's own Sec. 2.9 concedes the plane is often 'more akin to a segmentation mask than depth'—the central claim that parallax alone recovers geometrically accurate depth from micro-baseline data is falsified.

Watch

Extended reading notes

Core claim

The central claim is that carefully designed neural field models can compactly represent complex geometry and lighting from in-the-wild mobile photography, outperforming state-of-the-art methods without complex pre-processing, labeled ground truth, or machine learning priors. For micro-baseline depth, a plane-plus-offset implicit depth model fit jointly with an implicit image and a low-dimensional motion model recovers geometrically accurate depth from a two-second, 42-frame RAW burst. For layer separation, a two-plane alpha-composited model whose flows are controlled by neural spline fields separates occluders, reflections, shadows, and haze from background content. For image stitching, a n

Load-bearing premise

The depth recovery assumes the scene decomposes into a single static background plane plus small foreground depth offsets, and that a hand-tuned regularization weight picks the geometrically true depth among many photometrically equivalent solutions.

Editorial extensions

If this is right

If the depth claims hold, ordinary unstabilized phone captures become a viable source of dense, geometrically consistent depth without dedicated depth sensors or learned monocular priors.
Layer separation from a burst could turn reflection and occlusion removal into a standard post-capture operation, with the alpha matte produced automatically.
Neural spline fields provide a controllable flow representation that may generalize to other multi-frame fusion tasks such as denoising, deblurring, and video segmentation.
The neural light sphere model could make interactive panorama viewing—rather than static stitched images—a practical phone feature, with real-time rendering and modest model size.
The test-time optimization paradigm suggests that emerging sensors, such as hyperspectral or polarization imagers, could be handled without retraining on large datasets, by fitting the same kinds of self-regularized models to their raw data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The plane-plus-offset depth decomposition, which the paper itself notes is often a segmentation mask as much as a depth map, could be exploited more directly: the depth offset and plane can serve jointly for matting and compositing, a connection the thesis shows but leaves as an auxiliary application.
The dependence on hand-tuned hyperparameters (plane weight, encoding sizes, spline controls) hints at a future where these are selected automatically per scene, since the thesis demonstrates per-scene optimal settings differ.
The success of the spline flow representation on small-motion bursts suggests it could be adapted to video compression or editable video layers, where the spline's built-in temporal continuity would avoid flicker artifacts.
Because the methods fit raw sensor data without learned priors, they could be applied to non-smartphone imaging platforms—microscopes, telescopes, drones—by providing the appropriate projection and motion models, though the thesis stops short of demonstrating this.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The punchline: this is a well-written thesis that compiles three solid peer-reviewed papers (CVPR 2023, CVPR 2024, SIGGRAPH Asia 2024), but as a standalone arXiv submission it offers no new experimental results, and the abstract oversells what the body carefully hedges.

What it does well: the constituent works were real contributions—joint depth-plus-pose from micro-baseline bursts, the spline-parameterized flow representation, and the light-sphere model for stitching. The writing is clear and unusually honest. Fig. 2.11 and Sec. 2.9 concede that the photometric objective is degenerate and that the plane often acts as a segmentation mask rather than depth. The depth validation against structured-light scans is genuinely external.

The soft spots are real but proportional. First, the "outperform SOTA without priors" claim in the abstract is not supported. The plane-plus-offset model and the tuned weight α_p are priors, and the body shows they do the work in textureless and distant regions. That is a load-bearing caveat. Second, the synthetic layer-separation benchmark is generated by the authors' own pipeline under the same planar assumptions, so the PSNR gains partly confirm the model rather than test it. Third, the quantitative depth evaluation rests on four hand-selected objects with no error bars. None of these is fatal—the paper flags most of them—but together they mean the thesis is best read as a retrospective, not as a statement of new results.

Bottom line: if you work on mobile computational photography or test-time neural fields, the three chapters are worth knowing. As a submission, it deserves a serious referee only if the venue accepts a compilation with a corrected abstract; the components are non-trivial and the failure-mode analysis is useful. I'd bring it to a reading group when someone asks about micro-baseline depth, but I wouldn't cite the thesis itself—the individual papers are the better citation.

Referee Report

3 major / 5 minor

Summary. The dissertation proposes neural field models fitted at test time to raw smartphone burst and panoramic captures, addressing three tasks: micro-baseline depth estimation (Ch. 2), layer separation for occlusion/reflection/shadow removal (Ch. 3), and panoramic stitching with view synthesis (Ch. 4). The central claim is that carefully designed, self-regularized neural field representations, fit directly to raw sensor data by stochastic gradient descent, outperform state-of-the-art methods without labeled data, learned priors, or complex preprocessing. Each chapter presents a forward model, a neural field parameterization, real and synthetic evaluations, and an extensive set of qualitative results and ablations. The thesis is honest about many failure modes: dynamic, textureless, distant, translucent, and reflective scenes are documented in Sec. 2.10 and Fig. 2.16, and Sec. 2.9 explicitly concedes that the plane component often behaves as a segmentation mask rather than measured depth.

Significance. If the central thesis is correct, it would establish test-time neural field fitting as a broadly applicable alternative to supervised or pipeline-based computational photography, with the practical appeal of operating on raw captures from commodity phones. The work has several genuine strengths: it ships real captured datasets, records raw sensor data with gyroscope/metadata, provides open-source capture tools and project pages, validates depth on structured-light ground-truth scans (Fig. 2.7), and includes detailed ablations of encoding size, regularization, frame count, and motion scale. The three method chapters each introduce a compact representation (depth-on-a-plane, neural spline fields, neural light sphere) that is plausible and, within the tested conditions, often qualitatively superior to baselines. However, the abstract-level claim about being 'without... machine learning priors' and generally superior is significantly stronger than what the internal evidence supports, because the depth objective is shown to be degenerate and the selected solution is chosen by hand-tuned geometric and optimization priors. The contribution is still valuable as a demonstration of what thes

major comments (3)

[Sec. 2.5, Eq. (2.7), Eqs. (2.10)-(2.13), Fig. 2.11, Sec. 2.9] The central depth claim is not supported by the paper's own evidence. Fig. 2.11 shows two radically different depth maps with identical reprojection error, and Sec. 2.9 concedes that the plane component is often 'more akin to a segmentation mask than depth.' The depth model in Eq. (2.7) imposes a single plane plus ReLU offset, and the objective in Eqs. (2.10)-(2.13) with alpha_p = 1e-4 selects the solution among photometrically equivalent ones. Thus for textureless, distant, or non-planar regions, the recovered depth is not measured from parallax but assigned by a hand-tuned regularizer. This directly undercuts the abstract's claim of recovering depth 'without... machine learning priors' and 'self-regularized models.' The authors should reframe the depth contribution as prior-guided affine depth estimation, provide sensitivity of alpha_p across a broader range of scenes, and avoid claimi
[Sec. 2.11 and Sec. 3.6] Both synthetic benchmarks are generated under the same geometric assumptions as the proposed methods. In Sec. 2.11, synthetic scenes place a textured object in front of a tilted background plane, use the hand-shake paths measured by the authors' prior app, and render with the same small-angle camera model. In Sec. 3.6, transmission and obstruction planes are placed at Pi_z depths, composited with the same planar model and the same hand-shake data. Because the synthetic data is a special case of the model family, high quantitative scores there do not validate generalization to scenes that violate the plane-plus-offset or two-plane assumptions. The qualitative real-data results are useful, but the 'outperform SOTA' claim is not yet established for general in-the-wild captures.
[Ch. 3, Table 3.2 and Sec. 3.6] The layer separation chapter relies on task-specific configurations that are manually selected per application: flow encoding size, number of spline control points, plane depths, alpha regularization weight, and even separate columns for occlusion, reflection, shadow, and dehazing. The authors note these are 'not prescriptive' and that all neural scene fitting has per-scene parameters, but this undermines the thesis-level claim of a single 'well-constructed, self-regularized model.' To support the strong claim, the paper would need either a demonstration that one fixed configuration works across all tasks, or a clear admission that the method requires manual per-task or per-scene tuning.

minor comments (5)

[Abstract and Ch. 1] The phrase 'without relying on... machine learning priors' is technically about learned priors, but the method relies on hand-designed geometric priors (single-plane, spline smoothness, hash-grid resolution limits, regularization weights). Consider rewording to 'without learned priors or labeled data' and explicitly acknowledge hand-crafted regularizers.
[Sec. 2.5, Eq. (2.10)] The notation L = L_d + alpha_p (L_p/L_d) R is confusing because L_p and L_d are already defined as losses; the ratio may be intended as a per-sample weighting. Please clarify the indexing and whether the ratio is taken pointwise or as a scalar.
[Sec. 3.3.1] The claim that the neural spline field 'produces temporally consistent flow with no regularization' is supported mainly by the qualitative comparison in Fig. 3.2. Quantitative flow error against a reference estimator would strengthen the claim.
[Sec. 4.4.1] The comparison to traditional image stitching is presented visually and with limited metrics. Since the chapter claims 50 FPS rendering and 80 MB model size, a benchmark table of runtime, memory, and PSNR against classic stitchers would help.
[Throughout] There are minor typographical and formatting errors (e.g., 'frament' in Sec. 2.6, 'RA W' spacing, and the duplicated equation numbers in Ch. 4). These do not affect the science but should be cleaned up.

Circularity Check

1 steps flagged · score 3.0 of 10

Central derivations are self-contained, but the synthetic validation scenes are generated from the same plane/alpha-composite forward models, creating a partial validation loop.

other [Sec. 3.6 Synthetic Data Generation; cf. Sec. 2.11 Synthetic Evaluation]
"These are simulated as 3D planes in space at depths Π o z and Π t z respectively – Π o z < Π t z for occluders and Π o z > Π t z for reflectors – and apply a random tilt to the planes with angle θ∈[−20 ◦,20 ◦]. To generate realistic camera motion, we record samples of natural hand tremor with a pose-capture application built on the Apple ARKit library [42]. We then apply this motion path to a projective camera model, re-sample the image planes, and alpha-composite the outputs to produce the simulated burst stack."

The synthetic ground truth is produced by the same forward model the method optimizes: two image planes at depths Π_z, re-sampled by a projective camera with hand-shake motion, then alpha-composited — matching Eq. 3.10 (ĉ=(1−α)c_t+αc_o) and the plane/flow model of Sec. 3.3.2. Similarly, the Ch2 synthetic scenes (Sec. 2.11) are a plane background plus offset objects, matching the plane-plus-offset depth model of Eq. 2.7, with motion from the authors' own prior hand-shake capture. Thus 'near ground truth' reconstruction on these scenes is guaranteed up to optimization error by construction; these benchmarks cannot independently confirm the core plane/alpha-composite assumptions. The real-data comparisons (structured-light scans, tripod reference captures) remain external, so this is a partia

full rationale

The core derivations in Chapters 2–4 are not circular: depth, layer, and panorama models are fit to raw burst/panoramic data via photometric losses with explicit, disclosed regularizers (α_p, η_α), and the depth degeneracy is acknowledged in Fig. 2.11 and Sec. 2.9 rather than hidden. Real-world validation includes independent structured-light scans (Sec. 2.6) and tripod-reference captures (Sec. 3.4), so the central claims have external support. However, the synthetic validation loops are a genuine weakness: the Ch2 synthetic long-bursts place scanned objects in front of a plane and render with the authors' own hand-shake paths, exactly the plane-plus-offset scene model of Eq. 2.7; the Ch3 synthetic bursts are generated by alpha-compositing two planes with the same projective and motion model, exactly Eq. 3.10. Success on these scenes partly reflects that the data was constructed from the method's own assumptions, so the synthetic benchmarks cannot independently validate the plane/alpha-composite priors. This is a partial circularity in validation, not in the derivation of the methods, and the independent real-data results keep the central claims from being reduced to the model's own construction. The self-citations to Chugunov et al. [42] supply empirical hand-shake measurements rather than unverified uniqueness theorems, so they are not load-bearing in a circular way.

Assumptions & free parameters 9 free parameters · 9 assumptions · 0 invented entities

The central claims rest on a long list of hand-chosen knobs (α_p, η_α, η_r, encoding sizes, control points, plane depths) and on assumptions the paper itself documents as restrictive (static Lambertian scene, single background plane, common fate of layers, planar synthetic validation). The methods are honest test-time fits, so none of these are hidden; the ledger simply shows how much of the output is shaped by the choices of the authors rather than forced by the data. No new physical entities are introduced: "Neural Spline Field" and "Neural Light Sphere" are network architectures, not invented entities in the graviton sense.

free parameters (9)

plane regularization weight α_p = 1e-4
Chapter 2, Eqs. (2.10)-(2.13). Ablation (Fig. 2.14) shows the plane cutoff cuts into foreground geometry at α_p ≥ 1e-3 and drifts into background at α_p ≤ 1e-5; 1e-4 is a hand-picked compromise and directly shapes the output depth.
rotation offset weight η_r = 1e-4 (Ch. 2), 1e-3 (Ch. 4)
Weights the learned small-angle rotation correction against gyroscope readings (Eqs. 2.8-2.9, 4.2). Chosen by hand.
coarse-to-fine sweep constants k_min, k_max = -100, 200
Controls the schedule that masks hash-grid levels for depth (Eq. 2.14), determining which spatial frequencies of depth are admitted at each epoch.
Bezier control points N_c = 21 per curve
Chapter 2 motion model (Eq. 2.8). Ablation Fig. 2.15 shows too few control points blur depth, while N_c = 42 destabilizes training; 21 is the smallest that works.
depth hash encoding L_γd, N_max, T_γd = 8 levels, max 128, table 2^14
Chapter 2, Sec. 2.8. Ablation Fig. 2.12 shows L=16 bleeds image texture into depth and Fig. 2.13 shows 2^14 as the smallest table that preserves detail; selected per task.
per-task encoding sizes and loss weights (Ch. 3) = occlusion η_α=0.02; reflection η_α=0.0; shadow η_α=2.0; flow sizes |h|=11 or 15; plane depths Π_z per task
Table 3.2. Hand-selected per application; paper states "these model configurations are not prescriptive; all neural scene fitting approaches have per-scene optimal parameters" (Sec. 3.6).
alpha temperature τ_σ = not stated in available text
Sigmoid temperature in Eq. (3.10) controlling alpha transition sharpness; a free knob without a reported value.
ray perturbation weight η_p = decayed to zero over stage 1
Chapter 4, Eq. (4.9). Added to avoid local minima; magnitude not specified.
NeuLS hash grids γ1, γ2 = γ1: 8 levels, 4 to 112; γ2: 15 levels, 4 to 3145; table 2^19
Chapter 4, Sec. 4.3.5. Chosen to constrain spatial frequency of the ray-offset and view-dependent color models.

assumptions (9)

domain assumption Static, Lambertian scene model with known intrinsics and pinhole projection
Chapter 2 projection model (Eqs. 2.2-2.4) assumes a static world and view-consistent color; Sec. 2.10 documents that dynamic, translucent, and locally reflective scenes break the assumptions.
ad hoc to paper Background is a single plane plus foreground offset
Eq. (2.7) decomposes depth as plane plus ReLU offset; the paper concedes the plane is often a segmentation mask rather than measured geometry (Sec. 2.9).
ad hoc to paper Plane regularization resolves the photometric degeneracy
Eqs. (2.10)-(2.13) and Fig. 2.11: photometric loss alone cannot distinguish depth solutions; the hand-weighted plane penalty selects the solution.
domain assumption Common fate: obstructing and transmitted layers move differently and can be separated by two alpha-composited planes
Chapter 3 model (Eqs. 3.6-3.10); fails when occluder and transmission colors match (Fig. 3.20d) or parallax is insufficient.
ad hoc to paper Spline parametrization with low-resolution encodings prevents flow overfitting without explicit regularization
Sec. 3.3.1 and Fig. 3.24: Large flow encodings mix layers; the claim of "no regularization" depends on the hand-chosen encoding size and control-point count.
ad hoc to paper Ray-offset plus view-dependent color on a sphere explains parallax, motion, and lighting for panorama captures
Chapter 4 Eqs. (4.4)-(4.8); two-stage training and ray perturbations (Eq. 4.9) are required to avoid local minima, indicating the decomposition is not intrinsically stable.
ad hoc to paper Synthetic validation data is built under the model's own geometric assumptions
Sec. 3.6: transmission and obstruction simulated as planes at Π_z depths with hand-shake paths from the authors' ARKit app; the benchmark partially certifies the assumptions.
domain assumption RAW frames are linear with known black level and shading correction
Sec. 2.8 and Fig. 2.10: lens-shading compensation and linear Bayer interpolation are treated as calibrated inputs.
standard math Small-angle rotation approximation holds for hand tremor
Eqs. (2.9), (3.7), (4.2) parameterize rotation offsets as skew-symmetric small-angle matrices, valid for the measured millimeter-scale motion but explicitly breaking at 50cm motion (Fig. 3.20).

how reviews work

0 comments

Cite this review

Pith. "Pith review of Neural Field Representations of Mobile Computational Photography." pith.science (2026). https://pith.science/paper/E2AVE4YU

@misc{pith2026250805907,
  author       = {Pith},
  title        = {Pith review of: Neural Field Representations of Mobile Computational Photography},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E2AVE4YU}},
  note         = {Machine review of arXiv:2508.05907}
}

read the original abstract

Over the past two decades, mobile imaging has experienced a profound transformation, with cell phones rapidly eclipsing all other forms of digital photography in popularity. Today's cell phones are equipped with a diverse range of imaging technologies - laser depth ranging, multi-focal camera arrays, and split-pixel sensors - alongside non-visual sensors such as gyroscopes, accelerometers, and magnetometers. This, combined with on-board integrated chips for image and signal processing, makes the cell phone a versatile pocket-sized computational imaging platform. Parallel to this, we have seen in recent years how neural fields - small neural networks trained to map continuous spatial input coordinates to output signals - enable the reconstruction of complex scenes without explicit data representations such as pixel arrays or point clouds. In this thesis, I demonstrate how carefully designed neural field models can compactly represent complex geometry and lighting effects. Enabling applications such as depth estimation, layer separation, and image stitching directly from collected in-the-wild mobile photography data. These methods outperform state-of-the-art approaches without relying on complex pre-processing steps, labeled ground truth data, or machine learning priors. Instead, they leverage well-constructed, self-regularized models that tackle challenging inverse problems through stochastic gradient descent, fitting directly to raw measurements from a smartphone.

Figures

Figures reproduced from arXiv: 2508.05907 by the authors.

**Figure 1.1.** From fixed, tripod-stabilized systems to portable personal cameras and modern, [PITH_FULL_IMAGE:figures/full_fig_p010_1_1.png] view at source ↗

**Figure 1.2.** The modern cell phone is a portable computational imaging platform that fits as well into a research laboratory as well as it does into your pocket. shape the future of computer vision by enabling researchers to access richer, more varied datasets and richer, more varied problem spaces. Pocket Computational Imaging. For most people, the term “computational imaging” evokes the image of an optics table cluttered with… view at source ↗

**Figure 1.3.** (a) Without an aperture to block light, all rays from the scene reach the imaging [PITH_FULL_IMAGE:figures/full_fig_p014_1_3.png] view at source ↗

Figures from the paper (79 more)

**Figure 1.4.** Figure 1.4: A lens collects and focuses light from a point in the scene onto a point in the imaging plane, allowing a larger amount of light to form both a bright and sharp image. majority of the light from the scene, resulting in a very dark image – hence why the image plane o…

**Figure 1.5.** Figure 1.5: Simplified overview of image formation in a digital camera. Light enters through [PITH_FULL_IMAGE:figures/full_fig_p016_1_5.png]

**Figure 1.6.** Figure 1.6: Comparison of ways to increase light capture in low-light imaging and their [PITH_FULL_IMAGE:figures/full_fig_p017_1_6.png]

**Figure 1.7.** Figure 1.7: Illustration of color image formation in a digital camera. Incoming light passes [PITH_FULL_IMAGE:figures/full_fig_p018_1_7.png]

**Figure 1.8.** Figure 1.8: As each pixel in the Bayer CFA captures only one color (red, green, or blue), the data must be demosaiced to reconstruct a full RGB volume, with missing color values interpolated at each pixel location. Lastly, there is the topic of color imaging. Unfortunately, fabr…

**Figure 1.9.** Figure 1.9: Lens shading can occur as (a) offaxis light rays are partially blocked by the lens tube, resulting in lost light before reaching the sensor or (b) rays fall into dead areas of the sensor, not recorded by the photodiode. Lens shading is another issue requiring post-…

**Figure 1.10.** Figure 1.10: Rolling shutter effect caused by row-wise image readout. In a static scene, [PITH_FULL_IMAGE:figures/full_fig_p021_1_10.png]

**Figure 1.11.** Figure 1.11: The image of an pink-colored flower could plausibly correspond to either reality [PITH_FULL_IMAGE:figures/full_fig_p022_1_11.png]

**Figure 1.12.** Figure 1.12: Illustration of burst imaging for high dynamic range (HDR) reconstruction. A [PITH_FULL_IMAGE:figures/full_fig_p023_1_12.png]

**Figure 1.13.** Figure 1.13: (a) In the simplest case, where the camera undergoes only horizontal motion, [PITH_FULL_IMAGE:figures/full_fig_p024_1_13.png]

**Figure 1.14.** Figure 1.14: Illustration of 3D point reprojection between two camera views. A point (u1, v1) from camera corresponds to a point (x, y, z) in 3D space, that projects onto point (u2, v2) in the second camera view. 16 [PITH_FULL_IMAGE:figures/full_fig_p024_1_14.png]

**Figure 1.15.** Figure 1.15: Scene reconstruction challenges. (a) Even if the camera remains static, scene [PITH_FULL_IMAGE:figures/full_fig_p026_1_15.png]

**Figure 1.16.** Figure 1.16: Overview of a neural field model of a color image. An image coordinate ( [PITH_FULL_IMAGE:figures/full_fig_p027_1_16.png]

**Figure 1.17.** Figure 1.17: Overview of coordinate encoding. The input spatial coordinate is used to [PITH_FULL_IMAGE:figures/full_fig_p028_1_17.png]

**Figure 1.18.** Figure 1.18: Overview of multiresolution grid encoding. The input spatial coordinate is [PITH_FULL_IMAGE:figures/full_fig_p029_1_18.png]

**Figure 1.19.** Figure 1.19: Rather than storing values in a dense grid – where a value is stored for each location in a grid-sized table – a hash grid uses a hash function to map spatial coordinates to a smaller hash table. In this representation, the position of an entry in the table is no lo…

**Figure 2.1.** Figure 2.1: Our neural RGB-D model fits to a single long-burst image stack to distill high quality depth and camera motion. The model’s depth-on-a-plane decomposition can facilitate easy background masking, segmentation, and image compositing. items, and other complex 3D shapes,…

**Figure 2.2.** Figure 2.2: (a) The interface of our app for recording long-burst data. (b) Aligned RAW [PITH_FULL_IMAGE:figures/full_fig_p039_2_2.png]

**Figure 2.3.** Figure 2.3: A 2D example of reprojection and sampling. When a reference view (a) is [PITH_FULL_IMAGE:figures/full_fig_p042_2_3.png]

**Figure 2.4.** Figure 2.4: We model a long-burst capture as a single, fully-differentiable forward model [PITH_FULL_IMAGE:figures/full_fig_p042_2_4.png]

**Figure 2.5.** Figure 2.5: Ablation experiment on the effect of masked encoding levels. Using 4 encoding [PITH_FULL_IMAGE:figures/full_fig_p046_2_5.png]

**Figure 2.6.** Figure 2.6: Qualitative comparison of reconstruction results of indoor and outdoor scenes [PITH_FULL_IMAGE:figures/full_fig_p048_2_6.png]

**Figure 2.7.** Figure 2.7: Object reconstructions visualized as rendered meshes, with associated depth [PITH_FULL_IMAGE:figures/full_fig_p050_2_7.png]

**Figure 2.8.** Figure 2.8: Ablation study on the effects of fixing the image representation [PITH_FULL_IMAGE:figures/full_fig_p050_2_8.png]

**Figure 2.9.** Figure 2.9: The bayer color filter array on a camera sensor produces a spatially ”mosaicked” [PITH_FULL_IMAGE:figures/full_fig_p053_2_9.png]

**Figure 2.10.** Figure 2.10: (a) Lens shading is an effect caused by the geometry of the camera lens [PITH_FULL_IMAGE:figures/full_fig_p054_2_10.png]

**Figure 2.11.** Figure 2.11: In this example both Depth A and B produce [PITH_FULL_IMAGE:figures/full_fig_p056_2_11.png]

**Figure 2.12.** Figure 2.12: Ablation study on the effect of the number of levels [PITH_FULL_IMAGE:figures/full_fig_p057_2_12.png]

**Figure 2.13.** Figure 2.13: Ablation study on the effect of hash table size [PITH_FULL_IMAGE:figures/full_fig_p057_2_13.png]

**Figure 2.14.** Figure 2.14: Ablation study on the effects of regularization weight [PITH_FULL_IMAGE:figures/full_fig_p058_2_14.png]

**Figure 2.15.** Figure 2.15: Ablation study on the effect of the number of chosen control points [PITH_FULL_IMAGE:figures/full_fig_p058_2_15.png]

**Figure 2.16.** Figure 2.16: Depth reconstruction results for a set of challenging imaging scenarios. Not [PITH_FULL_IMAGE:figures/full_fig_p058_2_16.png]

**Figure 2.17.** Figure 2.17: Depth reconstruction results for long-bursts captured with normal (approx [PITH_FULL_IMAGE:figures/full_fig_p059_2_17.png]

**Figure 2.18.** Figure 2.18: (a) Objects which reflect local scene content, in this example a mirror-finish [PITH_FULL_IMAGE:figures/full_fig_p059_2_18.png]

**Figure 2.19.** Figure 2.19: Reconstruction on 7 additional scenes for our method and a mix of learned, [PITH_FULL_IMAGE:figures/full_fig_p061_2_19.png]

**Figure 2.20.** Figure 2.20: Depth reconstruction and motion estimation results for a set of simulated [PITH_FULL_IMAGE:figures/full_fig_p065_2_20.png]

**Figure 2.21.** Figure 2.21: Image and Depth Matting. Example of scene editing enabled by our plane [PITH_FULL_IMAGE:figures/full_fig_p066_2_21.png]

**Figure 2.22.** Figure 2.22: Hand-shake point clouds for individual volunteers. Each hand shake path is [PITH_FULL_IMAGE:figures/full_fig_p068_2_22.png]

**Figure 2.23.** Figure 2.23: Plot to illustrate the hand shake statistics presented in Table [PITH_FULL_IMAGE:figures/full_fig_p069_2_23.png]

**Figure 3.1.** Figure 3.1: Fitting our two-layer neural spline field model to a stack of images we’re able [PITH_FULL_IMAGE:figures/full_fig_p072_3_1.png]

**Figure 3.2.** Figure 3.2: Image and flow estimates for different representations of a short video sequence [PITH_FULL_IMAGE:figures/full_fig_p077_3_2.png]

**Figure 3.3.** Figure 3.3: Image fitting results for coordinate networks with [PITH_FULL_IMAGE:figures/full_fig_p079_3_3.png]

**Figure 3.4.** Figure 3.4: We model an input image sequence as the alpha composition of a [PITH_FULL_IMAGE:figures/full_fig_p082_3_4.png]

**Figure 3.5.** Figure 3.5: Reconstruction results for noisy, low-light conditions; exposure time 1/30, ISO [PITH_FULL_IMAGE:figures/full_fig_p083_3_5.png]

**Figure 3.6.** Figure 3.6: Occlusion removal results and estimated alpha maps for a set of captures with [PITH_FULL_IMAGE:figures/full_fig_p084_3_6.png]

**Figure 3.7.** Figure 3.7: Layer separation results in unique real-world cases enabled by our generalizable [PITH_FULL_IMAGE:figures/full_fig_p085_3_7.png]

**Figure 3.8.** Figure 3.8: Qualitative and quantitative obstruction removal results for a set of synthetic [PITH_FULL_IMAGE:figures/full_fig_p086_3_8.png]

**Figure 3.9.** Figure 3.9: Reflection removal results and estimated alpha maps for a set of captures with [PITH_FULL_IMAGE:figures/full_fig_p087_3_9.png]

**Figure 3.10.** Figure 3.10: Layer separation results for additional example applications: (a) shadow re [PITH_FULL_IMAGE:figures/full_fig_p088_3_10.png]

**Figure 3.11.** Figure 3.11: Learned flow estimator RAFT [186] and segmentation model SAM [94] struggle to produce meaningful outputs for a small-motion scene with an out-of-focus occluder. SAM successfully segments some objects behind the occluder (e.g., the statues on the building) but does n…

**Figure 3.12.** Figure 3.12: (a) Tripod-mounted occluder setup for capturing paired occlusion removal [PITH_FULL_IMAGE:figures/full_fig_p092_3_12.png]

**Figure 3.13.** Figure 3.13: Image fitting results for network encoding configurations as described in [PITH_FULL_IMAGE:figures/full_fig_p095_3_13.png]

**Figure 3.14.** Figure 3.14: Occlusion removal results and estimated alpha maps for a set of captures with [PITH_FULL_IMAGE:figures/full_fig_p096_3_14.png]

**Figure 3.15.** Figure 3.15: Reflection removal results and estimated alpha maps for a set of captures with [PITH_FULL_IMAGE:figures/full_fig_p097_3_15.png]

**Figure 3.16.** Figure 3.16: Shadow removal results under different lighting conditions: (a) partially diffuse, [PITH_FULL_IMAGE:figures/full_fig_p098_3_16.png]

**Figure 3.17.** Figure 3.17: Reflection removal results for challenging in-the-wild scenes: (a) storefront [PITH_FULL_IMAGE:figures/full_fig_p099_3_17.png]

**Figure 3.18.** Figure 3.18: Qualitative and quantitative occlusion removal results for a set of 3D rendered [PITH_FULL_IMAGE:figures/full_fig_p100_3_18.png]

**Figure 3.19.** Figure 3.19: Qualitative and quantitative reflection removal results for a set of 3D rendered [PITH_FULL_IMAGE:figures/full_fig_p101_3_19.png]

**Figure 3.20.** Figure 3.20: Challenging image reconstruction cases including varying scales of camera [PITH_FULL_IMAGE:figures/full_fig_p104_3_20.png]

**Figure 3.21.** Figure 3.21: Visualization of the effects of gradient loss [PITH_FULL_IMAGE:figures/full_fig_p105_3_21.png]

**Figure 3.22.** Figure 3.22: Ablation study on the effects of the number of input frames or duration of [PITH_FULL_IMAGE:figures/full_fig_p107_3_22.png]

**Figure 3.23.** Figure 3.23: Ablation study on the effects of alpha regularization weight [PITH_FULL_IMAGE:figures/full_fig_p108_3_23.png]

**Figure 3.24.** Figure 3.24: Ablation study on the effects of flow encoding size (Tab. [PITH_FULL_IMAGE:figures/full_fig_p108_3_24.png]

**Figure 3.25.** Figure 3.25: Demonstration of user-interactive scene editing facilitated by layer separation. [PITH_FULL_IMAGE:figures/full_fig_p109_3_25.png]

**Figure 4.1.** Figure 4.1: Fit during test-time directly to an input panoramic video capture, with no pre [PITH_FULL_IMAGE:figures/full_fig_p116_4_1.png]

**Figure 4.2.** Figure 4.2: Neural Light Sphere Model. Taking as input panoramic video capture I(u, v, n), we perform backward camera projection from a point X = (u, v) into a spherical hull to estimate an initial intersection point P. Ray offset model fr(P , X ˆ ) then bends this ray to a corr…

**Figure 4.3.** Figure 4.3: Hash Grid Spheres. In this 2D example we can observe how, for points on a circle, the number of accessed elements in the backing grid roughly doubles for a squaring of grid elements. Given an efficient mapping from grid location to element – e.g., hash table lookup –…

**Figure 4.4.** Figure 4.4 [PITH_FULL_IMAGE:figures/full_fig_p120_4_4.png]

**Figure 4.5.** Figure 4.5: Ray Perturbations. By applying small perturbations to ray origins O we are able to avoid hard-to-escape local minima solutions during early training epochs. In (a) we see how for the road, a region with low image texture, the No Perturbation example duplicates conten…

**Figure 4.6.** Figure 4.6: Data Capture. We develop an open-source Android-based mobile application to facilitate in-the-wild capture of scenes. The app’s settings allow for camera selection (main, ultrawide, or telephoto) and to either use the device’s auto-focus and auto-exposure features fo…

**Figure 4.7.** Figure 4.7: Scene Diversity. Shown above are spherical re-projections of reconstructions for a representative subset of scenes from our collected dataset. These include: (M) 1x main lens, (U) 0.5x ultrawide, (T) 5x telephoto, (L) low-light, (N) non-linear, and (360) full 360 deg…

**Figure 4.8.** Figure 4.8: Image Stitching Comparisons. Visualizing rectilinear projections of the stitched panoramas, we see that APAP [218] averages multiple frames in DarkDistillery to reduce noise, while ICE [130] segments and freezes the motion of pedestrians in Bluepit. Our proposed appr…

**Figure 4.9.** Figure 4.9: Low-light Reconstruction. Under low-light conditions, with sensor sensitivity at ISO 10,000 and exposure between 1/60s and 1/120s, our proposed model is able to not only successfully reconstruct but also considerably denoise the captured scene. We recommend the read…

**Figure 4.10.** Figure 4.10: Radiance Field Comparisons. Compared to radiance field approaches, including other multi-resolution hash-based [184, 139] and non-volume-integrating [92, 56] methods, we achieve significantly higher reconstruction quality over a range of settings. While Gaussian Spl…

**Figure 4.11.** Figure 4.11: Fast Occluders. Objects such as bikes and cars, which quickly enter and exit the field-of-view of the camera, pose a challenge for scene reconstruction as they cannot be compactly modeled as a view-dependent effect. Shown in the example above, during early training …

**Figure 4.12.** Figure 4.12: Model Component Analysis. Shown above are the effects on reconstruction of zeroing out the contribution of the view-dependent color model hd(γ1(X); θd), ray offset model fr(P , X ˆ ), or both models. We can observe that complex dynamic effects such as the steam clou…

**Figure 4.13.** Figure 4.13: Ray Offset Models. Comparing scene reconstruction results for various ray offset models, it’s clear from the No Ray Offset results that many scenes such as CatBar and Vending contain significant parallax effects that a sphere projection model alone cannot compensate…

**Figure 4.14.** Figure 4.14: Additional Radiance Field Comparisons. Reconstruction results for a highly detailed back-and-forth Bridge capture, night-time DarkPeace, and CityCars with fast-moving occluders. Scene titles are formatted as: Scene Name (Number of Captured Frames in Input) scene ele…

**Figure 4.15.** Figure 4.15: Preview Quality Reconstructions. Trained on 1/4 resolution inputs for 1/10th of the number of epochs, while they don’t reach the full reconstruction quality of the proposed method, these “Preview Quality” reconstructions take less than 30 seconds of training time pe…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

226 extracted references · 69 canonical work pages

[1]

Defocus deblurring using dual-pixel data

Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. InEuropean Conference on Computer Vision, pages 111–126. Springer, 2020

2020
[2]

De-fencing and multi-focus fusion using markov random field and image inpainting.IEEE Access, 10:35992–36005, 2022

Hannan Adeel, Muhammad Mohsin Riaz, and Syed Sohaib Ali. De-fencing and multi-focus fusion using markov random field and image inpainting.IEEE Access, 10:35992–36005, 2022

2022
[3]

Pyramid methods in image processing.RCA engineer, 29(6):33– 41, 1984

Edward H Adelson, Charles H Anderson, James R Bergen, Peter J Burt, and Joan M Ogden. Pyramid methods in image processing.RCA engineer, 29(6):33– 41, 1984

1984
[4]

Interactive digital photomontage

Aseem Agarwala, Mira Dontcheva, Maneesh Agrawala, Steven Drucker, Alex Colburn, Brian Curless, David Salesin, and Michael Cohen. Interactive digital photomontage. InACM SIGGRAPH 2004 Papers, pages 294–302. 2004

2004
[5]

Panoramic video tex- tures

Aseem Agarwala, Ke Colin Zheng, Chris Pal, Maneesh Agrawala, Michael Co- hen, Brian Curless, David Salesin, and Richard Szeliski. Panoramic video tex- tures. InACM SIGGRAPH 2005 Papers, pages 821–827. 2005

2005
[6]

Nonrigid structure from motion in trajectory space.Advances in neural information processing systems, 21, 2008

Ijaz Akhter, Yaser Sheikh, Sohaib Khan, and Takeo Kanade. Nonrigid structure from motion in trajectory space.Advances in neural information processing systems, 21, 2008

2008
[7]

Video from stills: Lensless imaging with rolling shutter

Nick Antipa, Patrick Oare, Emrah Bostan, Ren Ng, and Laura Waller. Video from stills: Lensless imaging with rolling shutter. In2019 IEEE International Conference on Computational Photography (ICCP), pages 1–8. IEEE, 2019

2019
[8]

Learning neural light fields with ray-space embedding

Benjamin Attal, Jia-Bin Huang, Michael Zollh¨ ofer, Johannes Kopf, and Changil Kim. Learning neural light fields with ray-space embedding. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19819–19829, 2022

2022

Show all 226 references

[9]

Matryodshka: Real-time 6dof video view synthesis using multi-sphere images

Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, and James Tompkin. Matryodshka: Real-time 6dof video view synthesis using multi-sphere images. InEuropean Conference on Computer Vision, pages 441–459. Springer, 2020. 136

2020
[10]

Explorable super resolution

Yuval Bahat and Tomer Michaeli. Explorable super resolution. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2716–2725, 2020

2020
[11]

Depth-aware video frame interpolation

Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming- Hsuan Yang. Depth-aware video frame interpolation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3703–3712, 2019

2019
[12]

Shape, illumination, and reflectance from shading.IEEE transactions on pattern analysis and machine intelligence, 37(8):1670–1687, 2014

Jonathan T Barron and Jitendra Malik. Shape, illumination, and reflectance from shading.IEEE transactions on pattern analysis and machine intelligence, 37(8):1670–1687, 2014

2014
[13]

Mip-nerf: A multiscale representa- tion for anti-aliasing neural radiance fields

Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representa- tion for anti-aliasing neural radiance fields. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages ...

2021
[14]

Zip-nerf: Anti-aliased grid-based neural radiance fields

Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Zip-nerf: Anti-aliased grid-based neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19697–19705, 2023

2023
[15]

Color imaging array.United States Patent 3,971,065, 1976

Bryce E Bayer. Color imaging array.United States Patent 3,971,065, 1976

1976
[16]

Om- niphotos: casual 360 vr photography.ACM Transactions on Graphics (TOG), 39(6):1–12, 2020

Tobias Bertel, Mingze Yuan, Reuben Lindroos, and Christian Richardt. Om- niphotos: casual 360 vr photography.ACM Transactions on Graphics (TOG), 39(6):1–12, 2020

2020
[17]

CRC press, 2021

Mario Bertero, Patrizia Boccacci, and Christine De Mol.Introduction to inverse problems in imaging. CRC press, 2021

2021
[18]

Deep burst super-resolution

Goutam Bhat, Martin Danelljan, Luc Van Gool, and Radu Timofte. Deep burst super-resolution. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9209–9218, 2021

2021
[19]

Adabins: Depth estimation using adaptive bins

Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka. Adabins: Depth estimation using adaptive bins. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4009–4018, 2021

2021
[20]

Color constancy using cnns

Simone Bianco, Claudio Cusano, and Raimondo Schettini. Color constancy using cnns. InProceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 81–89, 2015

2015
[21]

Smartphone imaging technology and its applications.Advanced Optical Technologies, 10(3):145–232, 2021

Vladan Blahnik and Oliver Schindelbeck. Smartphone imaging technology and its applications.Advanced Optical Technologies, 10(3):145–232, 2021

2021
[22]

High-precision wafer- level optics fabrication and integration.Photonics Spectra, 2011

Jens Bliedtner, Andreas Gebhardt, and Uwe Schmidt. High-precision wafer- level optics fabrication and integration.Photonics Spectra, 2011. 137

2011
[23]

John Wiley & Sons, 2006

Mary L Boas.Mathematical methods in the physical sciences. John Wiley & Sons, 2006

2006
[24]

W. S. Boyle and G. E. Smith. Charge coupled semiconductor devices.Bell System Technical Journal, 49(4):587–593, 1970

1970
[25]

Brainard and Anya C

David H. Brainard and Anya C. Hurlbert. Colour vision: Understanding the- dress.Current Biology, 25(13):R551–R554, 2015

2015
[26]

Unprocessing images for learned raw denoising

Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan T Barron. Unprocessing images for learned raw denoising. InProceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11036–11045, 2019

2019
[27]

Automatic panoramic image stitching using invariant features.International journal of computer vision, 74:59–73, 2007

Matthew Brown and David G Lowe. Automatic panoramic image stitching using invariant features.International journal of computer vision, 74:59–73, 2007

2007
[28]

Photo statistics: How many photos are taken every day?, 2024

Matic Broz. Photo statistics: How many photos are taken every day?, 2024. Accessed: 2024-12-10

2024
[29]

A spatial processor model for object colour perception

Gershon Buchsbaum. A spatial processor model for object colour perception. Journal of the Franklin Institute, 310(1):1–26, 1980

1980
[30]

Unstructured lumigraph rendering

Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen. Unstructured lumigraph rendering. InProceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 425–432, 2001

2001
[31]

A multiresolution spline with application to image mosaics.ACM Transactions on Graphics (TOG), 2(4):217–236, 1983

Peter J Burt and Edward H Adelson. A multiresolution spline with application to image mosaics.ACM Transactions on Graphics (TOG), 2(4):217–236, 1983

1983
[32]

Low-cost spad sensing for non-line-of-sight tracking, material classification and depth imaging

Clara Callenberg, Zheng Shi, Felix Heide, and Matthias B Hullin. Low-cost spad sensing for non-line-of-sight tracking, material classification and depth imaging. ACM Transactions on Graphics (TOG), 40(4):1–12, 2021

2021
[33]

Plenop- tic sampling

Jin-Xiang Chai, Xin Tong, Shing-Chow Chan, and Heung-Yeung Shum. Plenop- tic sampling. InProceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 307–318, 2000

2000
[34]

Pyramid stereo matching network

Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5410–5418, 2018

2018
[35]

Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo

Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124–14133, 2021. 138

2021
[36]

A simple model for intrinsic image decom- position with depth cues

Qifeng Chen and Vladlen Koltun. A simple model for intrinsic image decom- position with depth cues. InProceedings of the IEEE international conference on computer vision, pages 241–248, 2013

2013
[37]

View interpolation for image syn- thesis

Shenchang Eric Chen and Lance Williams. View interpolation for image syn- thesis. InProceedings of the 20th annual conference on Computer graphics and interactive techniques, pages 279–288, 1993

1993
[38]

The inverse problems you carry in your pocket.XRDS: Cross- roads, The ACM Magazine for Students, 31(2):44–49, 2025

Ilya Chugunov. The inverse problems you carry in your pocket.XRDS: Cross- roads, The ACM Magazine for Students, 31(2):44–49, 2025

2025
[39]

Neural light spheres for implicit image stitching and view synthesis

Ilya Chugunov, Amogh Joshi, Kiran Murthy, Francois Bleibel, and Felix Heide. Neural light spheres for implicit image stitching and view synthesis. InSIG- GRAPH Asia 2024 Conference Papers, pages 1–11, 2024

2024
[40]

Neural spline fields for burst image fusion and layer separation.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Ilya Chugunov, David Shustin, Ruyu Yan, Chenyang Lei, and Felix Heide. Neural spline fields for burst image fusion and layer separation.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2024
[41]

Shakes on a plane: Unsuper- vised depth estimation from unstabilized photography

Ilya Chugunov, Yuxuan Zhang, and Felix Heide. Shakes on a plane: Unsuper- vised depth estimation from unstabilized photography. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13240–13251, 2023

2023
[42]

The implicit values of a good hand shake: Handheld multi-frame neural depth refinement

Ilya Chugunov, Yuxuan Zhang, Zhihao Xia, Xuaner Zhang, Jiawen Chen, and Felix Heide. The implicit values of a good hand shake: Handheld multi-frame neural depth refinement. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 2852–2862, 2022

2022
[43]

Mednerf: Medical neural radi- ance fields for reconstructing 3d-aware ct-projections from a single x-ray

Abril Corona-Figueroa, Jonathan Frawley, Sam Bond-Taylor, Sarath Betha- pudi, Hubert PH Shum, and Chris G Willcocks. Mednerf: Medical neural radi- ance fields for reconstructing 3d-aware ct-projections from a single x-ray. In2022 44th Annual International Conference of the IEE...

2022
[44]

Mobile computational photography: A tour.arXiv preprint arXiv:2102.09000, 2021

Mauricio Delbracio, Damien Kelly, Michael S Brown, and Peyman Milanfar. Mobile computational photography: A tour.arXiv preprint arXiv:2102.09000, 2021

2021 arXiv
[45]

Imagenet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

2009
[46]

Depthlab: Real-time 3d interaction with depth maps for mobile augmented reality

Ruofei Du, Eric Turner, Maksym Dzitsiuk, Luca Prasso, Ivo Duarte, Jason Dourgarian, Joao Afonso, Jose Pascoal, Josh Gladstone, Nuno Cruces, et al. Depthlab: Real-time 3d interaction with depth maps for mobile augmented reality. InProceedings of the 33rd Annual ACM Symposium on...

2020
[47]

Claude E. Duchon. Lanczos filtering in one and two dimensions.Journal of Applied Meteorology, 18(8):1016–1022, 1979

1979
[48]

Depth map prediction from a single image using a multi-scale deep network

David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. pages 2366–2374, 2014

2014
[49]

¨Uber einen die erzeugung und verwandlung des lichtes betref- fenden heuristischen gesichtspunkt.Annalen der Physik, 17:132–148, 1905

Albert Einstein. ¨Uber einen die erzeugung und verwandlung des lichtes betref- fenden heuristischen gesichtspunkt.Annalen der Physik, 17:132–148, 1905

1905
[50]

Hyperdepth: Learning depth from structured light without matching

Sean Ryan Fanello, Christoph Rhemann, Vladimir Tankovich, Adarsh Kowdle, Sergio Orts Escolano, David Kim, and Shahram Izadi. Hyperdepth: Learning depth from structured light without matching. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, page...

2016
[51]

Image de- fencing framework with hybrid inpainting algorithm.Signal, Image and Video Processing, 10:1193–1201, 2016

Muhammad Shahid Farid, Arif Mahmood, and Marco Grangetto. Image de- fencing framework with hybrid inpainting algorithm.Signal, Image and Video Processing, 10:1193–1201, 2016

2016
[52]

Morgan Kaufmann, 2002

Gerald E Farin.Curves and surfaces for CAGD: a practical guide. Morgan Kaufmann, 2002

2002
[53]

Deepstereo: Learning to predict new views from the world’s imagery

John Flynn, Ivan Neulander, James Philbin, and Noah Snavely. Deepstereo: Learning to predict new views from the world’s imagery. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 5515–5524, 2016

2016
[54]

M4depth: A motion-based approach for monocular depth estimation on video sequences

Micha¨ el Fonder, Damien Ernst, and Marc Van Droogenbroeck. M4depth: A motion-based approach for monocular depth estimation on video sequences. arXiv preprint arXiv:2105.09847, 2021

2021 arXiv
[55]

Eric R. Fossum. Cmos active pixel image sensors: Past, present, and future. IEEE Transactions on Electron Devices, 44(10):1689–1698, 1997

1997
[56]

K-planes: Explicit radiance fields in space, time, and appearance

Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12479–12488, 2023

2023
[57]

Massively parallel multiview stereopsis by surface normal diffusion

Silvano Galliani, Katrin Lasinger, and Konrad Schindler. Massively parallel multiview stereopsis by surface normal diffusion. InProceedings of the IEEE International Conference on Computer Vision, pages 873–881, 2015

2015
[58]

Locally non-rigid registration for mobile hdr photography

Orazio Gallo, Alejandro Troccoli, Jun Hu, Kari Pulli, and Jan Kautz. Locally non-rigid registration for mobile hdr photography. InProceedings of the IEEE conference on computer vision and pattern recognition Workshops, pages 49–56, 2015. 140

2015
[59]

” double-dip”: unsuper- vised image decomposition via coupled deep-image-priors

Yosef Gandelsman, Assaf Shocher, and Michal Irani. ” double-dip”: unsuper- vised image decomposition via coupled deep-image-priors. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11026–11035, 2019

2019
[60]

Monocular dynamic view synthesis: A reality check.arXiv preprint arXiv:2210.13445, 2022

Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, and Angjoo Kanazawa. Monocular dynamic view synthesis: A reality check.arXiv preprint arXiv:2210.13445, 2022

2022 arXiv
[61]

Constructing image panora- mas using dual-homography warping

Junhong Gao, Seon Joo Kim, and Michael S Brown. Constructing image panora- mas using dual-homography warping. InCVPR 2011, pages 49–56. IEEE, 2011

2011
[62]

Seam-driven image stitching

Junhong Gao, Yu Li, Tat-Jun Chin, and Michael S Brown. Seam-driven image stitching. InEurographics (Short Papers), pages 45–48, 2013

2013
[63]

Deep burst denoising

Cl´ ement Godard, Kevin Matzen, and Matt Uyttendaele. Deep burst denoising. InProceedings of the European conference on computer vision (ECCV), pages 538–554, 2018

2018
[64]

See in the dark with night sight.https://blog.google/products/ pixel/see-light-night-sight/, 2018

Google. See in the dark with night sight.https://blog.google/products/ pixel/see-light-night-sight/, 2018. Accessed: 2023-10-24

2018
[65]

Astrophotography with night sight on pixel phones.https://blog

Google. Astrophotography with night sight on pixel phones.https://blog. research.google/2019/11/astrophotography-with-night-sight-on. html, 2019. Accessed: 2023-10-24

2019
[66]

The lumigraph

Steven J Gortler, Radek Grzeszczuk, Richard Szeliski, and Michael F Cohen. The lumigraph. InProceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 43–54, 1996

1996
[67]

Coded rolling shutter photography: Flexible space-time sampling

Jinwei Gu, Yasunobu Hitomi, Tomoo Mitsunaga, and Shree Nayar. Coded rolling shutter photography: Flexible space-time sampling. In2010 IEEE Inter- national Conference on Computational Photography (ICCP), pages 1–8. IEEE, 2010

2010
[68]

The relightables: Volumetric performance capture of humans with realistic relighting.ACM Transactions on Graphics (ToG), 38(6):1–19, 2019

Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting.ACM Transactions on Graphics (ToG), 3...

2019
[69]

Nerfren: Neural radiance fields with reflections

Yuan-Chen Guo, Di Kang, Linchao Bao, Yu He, and Song-Hai Zhang. Nerfren: Neural radiance fields with reflections. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR), pages 18409–18418, June 2022

2022
[70]

Fully automated image de-fencing using conditional generative adversarial networks, 2019

Divyanshu Gupta, Shorya Jain, Utkarsh Tripathi, Pratik Chattopadhyay, and Lipo Wang. Fully automated image de-fencing using conditional generative adversarial networks, 2019. 141

2019
[71]

High- quality depth from uncalibrated small motion clip

Hyowon Ha, Sunghoon Im, Jaesik Park, Hae-Gon Jeon, and In So Kweon. High- quality depth from uncalibrated small motion clip. InProceedings of the IEEE conference on computer vision and pattern Recognition, pages 5413–5421, 2016

2016
[72]

Image-based 3d object reconstruction: State-of-the-art and trends in the deep learning era

Xian-Feng Han, Hamid Laga, and Mohammed Bennamoun. Image-based 3d object reconstruction: State-of-the-art and trends in the deep learning era. IEEE transactions on pattern analysis and machine intelligence, 43(5):1578– 1604, 2019

2019
[73]

Springer Science & Business Media, 2012

Miles Hansard, Seungkyu Lee, Ouk Choi, and Radu Patrice Horaud.Time- of-flight cameras: principles, methods and applications. Springer Science & Business Media, 2012

2012
[74]

SIAM, 2006

Per Christian Hansen, James G Nagy, and Dianne P O’leary.Deblurring images: matrices, spectra, and filtering. SIAM, 2006

2006
[75]

Cambridge University Press, USA, 2 edition, 2003

Richard Hartley and Andrew Zisserman.Multiple View Geometry in Computer Vision. Cambridge University Press, USA, 2 edition, 2003

2003
[76]

Cambridge university press, 2003

Richard Hartley and Andrew Zisserman.Multiple view geometry in computer vision. Cambridge university press, 2003

2003
[77]

Burst photography for high dynamic range and low-light imaging on mobile cameras.ACM Transac- tions on Graphics (ToG), 35(6):1–12, 2016

Samuel W Hasinoff, Dillon Sharlet, Ryan Geiss, Andrew Adams, Jonathan T Barron, Florian Kainz, Jiawen Chen, and Marc Levoy. Burst photography for high dynamic range and low-light imaging on mobile cameras.ACM Transac- tions on Graphics (ToG), 35(6):1–12, 2016

2016
[78]

Rolling shutter bundle adjustment

Johan Hedborg, Per-Erik Forss´ en, Michael Felsberg, and Erik Ringaby. Rolling shutter bundle adjustment. In2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 1434–1441. IEEE, 2012

2012
[79]

Casual 3d photography.ACM Transactions on Graphics (TOG), 36(6):1–15, 2017

Peter Hedman, Suhib Alsisan, Richard Szeliski, and Johannes Kopf. Casual 3d photography.ACM Transactions on Graphics (TOG), 36(6):1–15, 2017

2017
[80]

Instant 3d photography.ACM Transactions on Graphics (TOG), 37(4):1–12, 2018

Peter Hedman and Johannes Kopf. Instant 3d photography.ACM Transactions on Graphics (TOG), 37(4):1–12, 2018

2018
[81]

Column-addressable and matrix-addressable multi-junction vcsel arrays for all electronic-scanning lidar

Eric Hegblom, Yeyu Zhu, Jun Yang, Kelvin Zhang, Benjamin Kesler, Lu- cas Morales, Matthew Peters, and Jay Skidmore. Column-addressable and matrix-addressable multi-junction vcsel arrays for all electronic-scanning lidar. InVertical-Cavity Surface-Emitting Lasers XXVI, volume 1...

2022
[82]

Keigo Hirakawa and Thomas W. Wolfe. Adaptive homogeneity-directed de- mosaicing algorithm. InIEEE Transactions on Image Processing, volume 14, pages 360–369. IEEE, 2005. 142

2005
[83]

An overview of depth cameras and range scanners based on time-of-flight technolo- gies.Machine vision and applications, 27(7):1005–1020, 2016

Radu Horaud, Miles Hansard, Georgios Evangelidis, and Cl´ ement M´ enier. An overview of depth cameras and range scanners based on time-of-flight technolo- gies.Machine vision and applications, 27(7):1005–1020, 2016

2016
[84]

Multilayer feedforward networks are universal approximators.Neural networks, 2(5):359–366, 1989

Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks are universal approximators.Neural networks, 2(5):359–366, 1989

1989
[85]

Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries

Junjie Hu, Mete Ozay, Yan Zhang, and Takayuki Okatani. Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries. In2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1043–1051. IEEE, 2019

2019
[86]

Single image reflection separation via compo- nent synergy

Qiming Hu and Xiaojie Guo. Single image reflection separation via compo- nent synergy. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 13138–13147, October 2023

2023
[87]

High quality structure from small motion for rolling shutter cameras

Sunghoon Im, Hyowon Ha, Gyeongmin Choe, Hae-Gon Jeon, Kyungdon Joo, and In So Kweon. High quality structure from small motion for rolling shutter cameras. InProceedings of the IEEE International Conference on Computer Vision, pages 837–845, 2015

2015
[88]

Calibrated rgb-d salient object detection

Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, et al. Calibrated rgb-d salient object detection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9471–9481, 2021

2021
[89]

Micro-baseline stereo.Technical Report MSR-TR-2014–73, page 8, 2014

Neel Joshi and C Lawrence Zitnick. Micro-baseline stereo.Technical Report MSR-TR-2014–73, page 8, 2014

2014
[90]

Deep high dynamic range imaging of dynamic scenes.ACM Trans

Nima Khademi Kalantari, Ravi Ramamoorthi, et al. Deep high dynamic range imaging of dynamic scenes.ACM Trans. Graph., 36(4):144–1, 2017

2017
[91]

Layered neural atlases for consistent video editing.ACM Transactions on Graphics (TOG), 40(6):1– 12, 2021

Yoni Kasten, Dolev Ofri, Oliver Wang, and Tali Dekel. Layered neural atlases for consistent video editing.ACM Transactions on Graphics (TOG), 40(6):1– 12, 2021

2021
[92]

3d gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4):1–14, 2023

Bernhard Kerbl, Georgios Kopanas, Thomas Leimk¨ uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4):1–14, 2023

2023
[93]

Adam: A method for stochastic optimiza- tion.arXiv preprint arXiv:1412.6980, 2014

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimiza- tion.arXiv preprint arXiv:1412.6980, 2014

2014 arXiv
[94]

Segment anything

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015–4026, 2023. 143

2023
[95]

The tensor algebra compiler.Proceedings of the ACM on Pro- gramming Languages, 1(OOPSLA):1–29, 2017

Fredrik Kjolstad, Shoaib Kamil, Stephen Chou, David Lugato, and Saman Amarasinghe. The tensor algebra compiler.Proceedings of the ACM on Pro- gramming Languages, 1(OOPSLA):1–29, 2017

2017
[96]

Time-of- flight cameras in computer graphics

Andreas Kolb, Erhardt Barth, Reinhard Koch, and Rasmus Larsen. Time-of- flight cameras in computer graphics. InComputer Graphics Forum, volume 29, pages 141–159. Wiley Online Library, 2010

2010
[97]

One shot 3d photography.ACM Transactions on Graphics (TOG), 39(4):76–1, 2020

Johannes Kopf, Kevin Matzen, Suhib Alsisan, Ocean Quigley, Francis Ge, Yang- ming Chong, Josh Patterson, Jan-Michael Frahm, Shu Wu, Matthew Yu, et al. One shot 3d photography.ACM Transactions on Graphics (TOG), 39(4):76–1, 2020

2020
[98]

Robust consistent video depth estimation

Johannes Kopf, Xuejian Rong, and Jia-Bin Huang. Robust consistent video depth estimation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1611–1621, 2021

2021
[99]

Single image fence removal using fast fourier transform

Keitaro Kume and Masaaki Ikehara. Single image fence removal using fast fourier transform. In2023 IEEE International Conference on Consumer Elec- tronics (ICCE), pages 1–5, 2023

2023
[100]

Real-time panoramic depth maps from omni-directional stereo images for 6 dof videos in virtual reality

Po Kong Lai, Shuang Xie, Jochen Lang, and Robert Lagani` ere. Real-time panoramic depth maps from omni-directional stereo images for 6 dof videos in virtual reality. In2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pages 405–412. IEEE, 2019

2019
[101]

3d time-of-flight distance measurement with custom solid-state image sensors in cmos/ccd-technology

Robert Lange. 3d time-of-flight distance measurement with custom solid-state image sensors in cmos/ccd-technology. 2000

2000
[102]

Fast dynamic 3d mr spectroscopic imaging with compressed sensing and multiband excitation pulses for hyperpolarized 13c studies.Magnetic resonance in medicine, 65(3):610–619, 2011

Peder EZ Larson, Simon Hu, Michael Lustig, Adam B Kerr, Sarah J Nelson, John Kurhanewicz, John M Pauly, and Daniel B Vigneron. Fast dynamic 3d mr spectroscopic imaging with compressed sensing and multiband excitation pulses for hyperpolarized 13c studies.Magnetic resonance in ...

2011
[103]

High dy- namic range and super-resolution from raw image bursts.arXiv preprint arXiv:2207.14671, 2022

Bruno Lecouat, Thomas Eboli, Jean Ponce, and Julien Mairal. High dy- namic range and super-resolution from raw image bursts.arXiv preprint arXiv:2207.14671, 2022

2022 arXiv
[104]

Noise2noise: Learning image restoration without clean data.arXiv preprint arXiv:1803.04189, 2018

Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2noise: Learning image restoration without clean data.arXiv preprint arXiv:1803.04189, 2018

2018 arXiv
[105]

Robust reflection removal with reflection-free flash-only cues

Chenyang Lei and Qifeng Chen. Robust reflection removal with reflection-free flash-only cues. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 144

2021
[106]

Polarized reflection removal with perfect alignment in the wild

Chenyang Lei, Xuhua Huang, Mengdi Zhang, Qiong Yan, Wenxiu Sun, and Qifeng Chen. Polarized reflection removal with perfect alignment in the wild. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1750–1758, 2020

2020
[107]

Robust reflection removal with flash-only cues in the wild, 2022

Chenyang Lei, Xudong Jiang, and Qifeng Chen. Robust reflection removal with flash-only cues in the wild, 2022

2022
[108]

Light field rendering

Marc Levoy and Pat Hanrahan. Light field rendering. InProceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’96, page 31–42, New York, NY, USA, 1996. Association for Com- puting Machinery

1996
[109]

Yu Li and Michael S. Brown. Exploiting reflection change for automatic reflec- tion removal. InProceedings of the IEEE International Conference on Computer Vision (ICCV), December 2013

2013
[110]

Neuralangelo: High-fidelity neural surface reconstruction

Zhaoshuo Li, Thomas M¨ uller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin. Neuralangelo: High-fidelity neural surface reconstruction. InProceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 8456–8465, 2023

2023
[111]

Dynibar: Neural dynamic image-based rendering

Zhengqi Li, Qianqian Wang, Forrester Cole, Richard Tucker, and Noah Snavely. Dynibar: Neural dynamic image-based rendering. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4273–4284, 2023

2023
[112]

Handheld mobile photography in very low light.ACM Trans

Orly Liba, Kiran Murthy, Yun-Ta Tsai, Tim Brooks, Tianfan Xue, Nikhil Kar- nad, Qiurui He, Jonathan T Barron, Dillon Sharlet, Ryan Geiss, et al. Handheld mobile photography in very low light.ACM Trans. Graph., 38(6):164–1, 2019

2019
[113]

Barf: Bundle-adjusting neural radiance fields

Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. Barf: Bundle-adjusting neural radiance fields. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 5741–5751, 2021

2021
[114]

Deep multi depth panoramas for view synthesis

Kai-En Lin, Zexiang Xu, Ben Mildenhall, Pratul P Srinivasan, Yannick Hold- Geoffroy, Stephen DiVerdi, Qi Sun, Kalyan Sunkavalli, and Ravi Ramamoorthi. Deep multi depth panoramas for view synthesis. InEuropean Conference on Computer Vision, pages 328–344. Springer, 2020

2020
[115]

Microsoft coco: Common ob- jects in context

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ ar, and C Lawrence Zitnick. Microsoft coco: Common ob- jects in context. InComputer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedi...

2014
[116]

Raft-stereo: Multilevel recurrent field transforms for stereo matching.arXiv preprint arXiv:2109.07547, 2021

Lahav Lipson, Zachary Teed, and Jia Deng. Raft-stereo: Multilevel recurrent field transforms for stereo matching.arXiv preprint arXiv:2109.07547, 2021. 145

2021 arXiv
[117]

Learning to see through obstructions

Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, and Jia-Bin Huang. Learning to see through obstructions. InIEEE Conference on Computer Vision and Pattern Recognition, 2020

2020
[118]

Semantic guided single image reflection removal, 2022

Yunfei Liu, Yu Li, Shaodi You, and Feng Lu. Semantic guided single image reflection removal, 2022

2022
[119]

Distinctive image features from scale-invariant keypoints.In- ternational journal of computer vision, 60:91–110, 2004

David G Lowe. Distinctive image features from scale-invariant keypoints.In- ternational journal of computer vision, 60:91–110, 2004

2004
[120]

Omnimatte: Associating objects and their effects in video

Erika Lu, Forrester Cole, Tali Dekel, Andrew Zisserman, William T Freeman, and Michael Rubinstein. Omnimatte: Associating objects and their effects in video. InCVPR, 2021

2021
[121]

Evaluation of the apple iphone 12 pro lidar for an application in geosciences.Scientific reports, 11(1):1– 9, 2021

Gregor Luetzenburg, Aart Kroon, and Anders A Bjørk. Evaluation of the apple iphone 12 pro lidar for an application in geosciences.Scientific reports, 11(1):1– 9, 2021

2021
[122]

Consistent video depth estimation.ACM Transactions on Graphics (TOG), 39(4):71–1, 2020

Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes Kopf. Consistent video depth estimation.ACM Transactions on Graphics (TOG), 39(4):71–1, 2020

2020
[123]

Malvar, Li-wei He, and Ross Cutler

Henrique S. Malvar, Li-wei He, and Ross Cutler. High-quality linear inter- polation for demosaicing of bayer-patterned color images. InProceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 485–488. IEEE, 2004

2004
[124]

Cooperative computation of stereo disparity: A cooperative algorithm is derived for extracting disparity information from stereo image pairs.Science, 194(4262):283–287, 1976

David Marr and Tomaso Poggio. Cooperative computation of stereo disparity: A cooperative algorithm is derived for extracting disparity information from stereo image pairs.Science, 194(4262):283–287, 1976

1976
[125]

Nerf in the wild: Neural radiance fields for unconstrained photo collections

Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collections. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition...

2021
[126]

Long-range time-of-flight scanning sen- sor based on high-speed time-correlated single-photon counting.Applied optics, 48(32):6241–6251, 2009

Aongus McCarthy, Robert J Collins, Nils J Krichel, Ver´ onica Fern´ andez, An- drew M Wallace, and Gerald S Buller. Long-range time-of-flight scanning sen- sor based on high-speed time-correlated single-photon counting.Applied optics, 48(32):6241–6251, 2009

2009
[127]

Plenoptic modeling: an image-based ren- dering system

Leonard McMillan and Gary Bishop. Plenoptic modeling: an image-based ren- dering system. InProceedings of the 22nd annual conference on Computer graphics and interactive techniques, pages 39–46, 1995

1995
[128]

Exposure fusion: A simple and practical alternative to high dynamic range photography

Tom Mertens, Jan Kautz, and Frank Van Reeth. Exposure fusion: A simple and practical alternative to high dynamic range photography. InComputer graphics forum, volume 28, pages 161–171. Wiley Online Library, 2009. 146

2009
[129]

Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging

S Mahdi H Miangoleh, Sebastian Dille, Long Mai, Sylvain Paris, and Yagiz Aksoy. Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pag...

2021
[130]

Microsoft image composite editor

Microsoft Research. Microsoft image composite editor. https://www.microsoft.com/en-us/research/product/computational- photography-applications/image-composite-editor, 2015. Version 2.0.3, released in 2015

2015
[131]

Burst denoising with kernel prediction networks

Ben Mildenhall, Jonathan T Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, and Robert Carroll. Burst denoising with kernel prediction networks. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 2502– 2510, 2018

2018
[132]

Nerf in the dark: High dynamic range view synthe- sis from noisy raw images

Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul P Srinivasan, and Jonathan T Barron. Nerf in the dark: High dynamic range view synthe- sis from noisy raw images. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16190–16199, 2022

2022
[133]

Nerf: Representing scenes as neural radiance fields for view synthesis

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. InEuropean conference on computer vision, pages 405–421. Springer, 2020

2020
[134]

Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99–106, 2021

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99–106, 2021

2021
[135]

Modern aug- mented reality: Applications, trends, and future directions.arXiv preprint arXiv:2202.09450, 2022

Shervin Minaee, Xiaodan Liang, and Shuicheng Yan. Modern aug- mented reality: Applications, trends, and future directions.arXiv preprint arXiv:2202.09450, 2022

2022 arXiv
[136]

Dancing under the stars: video denoising in starlight

Kristina Monakhova, Stephan R Richter, Laura Waller, and Vladlen Koltun. Dancing under the stars: video denoising in starlight. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16241–16251, 2022

2022
[137]

Megapixel time-gated spad image sensor for 2d and 3d imaging applications.Optica, 7(4):346–354, 2020

Kazuhiro Morimoto, Andrei Ardelean, Ming-Lo Wu, Arin Can Ulku, Ivan Michel Antolovic, Claudio Bruschini, and Edoardo Charbon. Megapixel time-gated spad image sensor for 2d and 3d imaging applications.Optica, 7(4):346–354, 2020

2020
[138]

Neural surface maps

Luca Morreale, Noam Aigerman, Vladimir G Kim, and Niloy J Mitra. Neural surface maps. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4639–4648, 2021. 147

2021
[139]

Instant neural graphics primitives with a multiresolution hash encoding.ACM trans- actions on graphics (TOG), 41(4):1–15, 2022

Thomas Muller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding.ACM trans- actions on graphics (TOG), 41(4):1–15, 2022

2022
[140]

Real- time neural radiance caching for path tracing.arXiv preprint arXiv:2106.12372, 2021

Thomas M¨ uller, Fabrice Rousselle, Jan Nov´ ak, and Alexander Keller. Real- time neural radiance caching for path tracing.arXiv preprint arXiv:2106.12372, 2021

2021 arXiv
[141]

Neural image representations for multi-image fusion and layer separation

Seonghyeon Nam, Marcus A Brubaker, and Michael S Brown. Neural image representations for multi-image fusion and layer separation. InEuropean con- ference on computer vision, pages 216–232. Springer, 2022

2022
[142]

PhD thesis, Stanford university, 2005

Ren Ng, Marc Levoy, Mathieu Br´ edif, Gene Duval, Mark Horowitz, and Pat Hanrahan.Light field photography with a hand-held plenoptic camera. PhD thesis, Stanford university, 2005

2005
[143]

Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs

Michael Niemeyer, Jonathan T Barron, Ben Mildenhall, Mehdi SM Sajjadi, Andreas Geiger, and Noha Radwan. Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, page...

2022
[144]

Barron, Neal Wadhwa, Rahul Garg, Feng Liu, and Tianfan Xue

Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron, Neal Wadhwa, Rahul Garg, Feng Liu, and Tianfan Xue. Learned dual-view reflection removal, 2020

2020
[145]

Total relighting: learning to relight portraits for background replacement.ACM Transactions on Graphics (TOG), 40(4):1–21, 2021

Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement.ACM Transactions on Graphics (TOG), 40(4):1–21, 2021

2021
[146]

Image de- fencing revisited

Minwoo Park, Kyle Brocklehurst, Robert T Collins, and Yanxi Liu. Image de- fencing revisited. InComputer Vision–ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part IV 10, pages 422–434. Springer, 2011

2010
[147]

Nan: Noise-aware nerfs for burst-denoising

Naama Pearl, Tali Treibitz, and Simon Korman. Nan: Noise-aware nerfs for burst-denoising. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12672–12681, 2022

2022
[148]

Lecture 11: Camera processing pipeline.CS231M: Mobile Computer Vision, May 2015

Kari Pulli. Lecture 11: Camera processing pipeline.CS231M: Mobile Computer Vision, May 2015

2015
[149]

Rethinking the pipeline of demosaicing, de- noising and super-resolution.arXiv preprint arXiv:1905.02538, 2019

Guocheng Qian, Yuanhao Wang, Chao Dong, Jimmy S Ren, Wolfgang Heidrich, Bernard Ghanem, and Jinjin Gu. Rethinking the pipeline of demosaicing, de- noising and super-resolution.arXiv preprint arXiv:1905.02538, 2019. 148

1905 arXiv
[150]

Infinite photorealistic worlds using procedural generation

Alexander Raistrick, Lahav Lipson, Zeyu Ma, Lingjie Mei, Mingzhe Wang, Yiming Zuo, Karhan Kayan, Hongyu Wen, Beining Han, Yihan Wang, et al. Infinite photorealistic worlds using procedural generation. InProceedings of the IEEE/CVF conference on computer vision and pattern reco...

2023
[151]

Vision transformers for dense prediction

Ren´ e Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 12179–12188, 2021

2021
[152]

Towards robust monocular depth estimation: Mixing datasets for zero- shot cross-dataset transfer.IEEE transactions on pattern analysis and machine intelligence, 2020

Ren´ e Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero- shot cross-dataset transfer.IEEE transactions on pattern analysis and machine intelligence, 2020

2020
[153]

Unwrap mosaics: A new representation for video editing

Alex Rav-Acha, Pushmeet Kohli, Carsten Rother, and Andrew Fitzgibbon. Unwrap mosaics: A new representation for video editing. InACM SIGGRAPH 2008 papers, pages 1–11. 2008

2008
[154]

The camera obscura–a chronicle, 1982

PE Richmond. The camera obscura–a chronicle, 1982

1982
[155]

High-resolution image synthesis with latent diffusion models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj¨ orn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022

2022
[156]

From coarse to fine: Robust hierarchical localization at large scale

Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, and Marcin Dymczyk. From coarse to fine: Robust hierarchical localization at large scale. InProceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12716–12725, 2019

2019
[157]

Superglue: Learning feature matching with graph neural networks

Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Ra- binovich. Superglue: Learning feature matching with graph neural networks. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4938–4947, 2020

2020
[158]

High-accuracy stereo depth maps using structured light

Daniel Scharstein and Richard Szeliski. High-accuracy stereo depth maps using structured light. In2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., volume 1, pages I–I. IEEE, 2003

2003
[159]

Structure-from-motion revis- ited

Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revis- ited. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 4104–4113, 2016

2016
[160]

Image processing for quality improvement.Proceedings of the IEEE, 66(12):1640–1651, 1978

William F Schreiber. Image processing for quality improvement.Proceedings of the IEEE, 66(12):1640–1651, 1978. 149

1978
[161]

Graf: Gen- erative radiance fields for 3d-aware image synthesis.Advances in Neural Infor- mation Processing Systems, 33:20154–20166, 2020

Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Graf: Gen- erative radiance fields for 3d-aware image synthesis.Advances in Neural Infor- mation Processing Systems, 33:20154–20166, 2020

2020
[162]

Rgb-d object detection and semantic segmentation for autonomous manipulation in clutter.The International Journal of Robotics Research, 37(4-5):437–451, 2018

Max Schwarz, Anton Milan, Arul Selvam Periyasamy, and Sven Behnke. Rgb-d object detection and semantic segmentation for autonomous manipulation in clutter.The International Journal of Robotics Research, 37(4-5):437–451, 2018

2018
[163]

Motion parallax for 360 rgbd video.IEEE Transactions on Visualization and Computer Graphics, 25(5):1817–1827, 2019

Ana Serrano, Incheol Kim, Zhili Chen, Stephen DiVerdi, Diego Gutierrez, Aaron Hertzmann, and Belen Masia. Motion parallax for 360 rgbd video.IEEE Transactions on Visualization and Computer Graphics, 25(5):1817–1827, 2019

2019
[164]

Layered depth images

Jonathan Shade, Steven Gortler, Li-wei He, and Richard Szeliski. Layered depth images. InProceedings of the 25th annual conference on Computer graphics and interactive techniques, pages 231–242, 1998

1998
[165]

Light field reflection and background separation network based on adaptive focus selection.IEEE Transactions on Computational Imaging, 9:435–447, 2023

Zeqi Shen, Shuo Zhang, and Youfang Lin. Light field reflection and background separation network based on adaptive focus selection.IEEE Transactions on Computational Imaging, 9:435–447, 2023

2023
[166]

The interpretation of aerial photographs.Royal United Services Institution

GJV Shepherd. The interpretation of aerial photographs.Royal United Services Institution. Journal, 70(478):279–287, 1925

1925
[167]

Split-aperture 2-in-1 computational cameras.ACM Transactions on Graphics (TOG), 43(4):1–19, 2024

Zheng Shi, Ilya Chugunov, Mario Bijelic, Geoffroi Cˆ ot´ e, Jiwoon Yeom, Qiang Fu, Hadi Amata, Wolfgang Heidrich, and Felix Heide. Split-aperture 2-in-1 computational cameras.ACM Transactions on Graphics (TOG), 43(4):1–19, 2024

2024
[168]

YiChang Shih, Dilip Krishnan, Fredo Durand, and William T. Freeman. Re- flection removal using ghosting cues. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

2015
[169]

Rendering with concentric mosaics

Heung-Yeung Shum and Li-Wei He. Rendering with concentric mosaics. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 299–306, 1999

1999
[170]

Construction of panoramic image mosaics with global and local alignment.International Journal of Computer Vision, 48(2):151–152, 2002

Heung-Yeung Shum and Richard Szeliski. Construction of panoramic image mosaics with global and local alignment.International Journal of Computer Vision, 48(2):151–152, 2002

2002
[171]

Multi-view stereo via graph cuts on the dual of an adaptive tetrahedral mesh

Sudipta N Sinha, Philippos Mordohai, and Marc Pollefeys. Multi-view stereo via graph cuts on the dual of an adaptive tetrahedral mesh. In2007 IEEE 11th International Conference on Computer Vision, pages 1–8. IEEE, 2007

2007
[172]

Implicit neural representations with periodic activation func- tions.Advances in neural information processing systems, 33:7462–7473, 2020

Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gor- don Wetzstein. Implicit neural representations with periodic activation func- tions.Advances in neural information processing systems, 33:7462–7473, 2020. 150

2020
[173]

Deepvoxels: Learning persistent 3d feature em- beddings

Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wet- zstein, and Michael Zollhofer. Deepvoxels: Learning persistent 3d feature em- beddings. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2437–2446, 2019

2019
[174]

Andrew Stockman and Lindsay T. Sharpe. Spectral sensitivities of the human cones.Journal of the Optical Society of America A, 16(3):531–543, 1999

1999
[175]

Light field neural rendering

Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Light field neural rendering. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 8269–8279, 2022

2022
[176]

Coil: Coordinate-based internal learning for tomographic imaging.IEEE Trans- actions on Computational Imaging, 7:1400–1412, 2021

Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, and Ulugbek S Kamilov. Coil: Coordinate-based internal learning for tomographic imaging.IEEE Trans- actions on Computational Imaging, 7:1400–1412, 2021

2021
[177]

Resolution-robust large mask inpainting with fourier convolutions.arXiv preprint arXiv:2109.07161, 2021

Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, and Victor Lempitsky. Resolution-robust large mask inpainting with fourier convolutions.arXiv preprint arXiv:2109.07161, 2021

2021 arXiv
[178]

Image alignment and stitching: A tutorial.Foundations and Trends®in Computer Graphics and Vision, 2(1):1–104, 2007

Richard Szeliski et al. Image alignment and stitching: A tutorial.Foundations and Trends®in Computer Graphics and Vision, 2(1):1–104, 2007

2007
[179]

Creating full view panoramic image mosaics and environment maps

Richard Szeliski and Heung-Yeung Shum. Creating full view panoramic image mosaics and environment maps. InProceedings of the 24th annual conference on Computer graphics and interactive techniques, pages 251–258, 1997

1997
[180]

Compact neural graphics primitives with learned hash probing

Towaki Takikawa, Thomas M¨ uller, Merlin Nimier-David, Alex Evans, Sanja Fi- dler, Alec Jacobson, and Alexander Keller. Compact neural graphics primitives with learned hash probing. InSIGGRAPH Asia 2023 Conference Papers, pages 1–10, 2023

2023
[181]

Joint demosaicing and denoising of noisy bayer images with admm

Hanlin Tan, Xiangrong Zeng, Shiming Lai, Yu Liu, and Maojun Zhang. Joint demosaicing and denoising of noisy bayer images with admm. In2017 IEEE International Conference on Image Processing (ICIP), pages 2951–2955. IEEE, 2017

2017
[182]

Block-nerf: Scalable large scene neural view synthesis

Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Milden- hall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-nerf: Scalable large scene neural view synthesis. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn...

2022
[183]

Fourier features let networks learn high frequency functions in low dimensional domains.Advances in Neural Information Processing Systems, 33:7537–7547, 2020

Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains.Advances in Neural Information Proce...

2020
[184]

Nerfstudio: A modular framework for neural radiance field development

Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, et al. Nerfstudio: A modular framework for neural radiance field development. InACM SIGGRAPH 2023 Conference Proceedings, pages 1–12, 2023

2023
[185]

Hitnet: Hierarchical iterative tile refinement net- work for real-time stereo matching

Vladimir Tankovich, Christian Hane, Yinda Zhang, Adarsh Kowdle, Sean Fanello, and Sofien Bouaziz. Hitnet: Hierarchical iterative tile refinement net- work for real-time stereo matching. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages ...

2021
[186]

Raft: Recurrent all-pairs field transforms for optical flow

Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. InComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pages 402–419. Springer, 2020

2020
[187]

Raft-3d: Scene flow using rigid-motion embeddings

Zachary Teed and Jia Deng. Raft-3d: Scene flow using rigid-motion embeddings. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8375–8384, 2021

2021
[188]

Multiplexed coded illumination for fourier ptychography with an led array microscope.Biomedical optics express, 5(7):2376–2389, 2014

Lei Tian, Xiao Li, Kannan Ramchandran, and Laura Waller. Multiplexed coded illumination for fourier ptychography with an led array microscope.Biomedical optics express, 5(7):2376–2389, 2014

2014
[189]

Bundle adjustment—a modern synthesis

Bill Triggs, Philip F McLauchlan, Richard I Hartley, and Andrew W Fitzgibbon. Bundle adjustment—a modern synthesis. InInternational workshop on vision algorithms, pages 298–372. Springer, 1999

1999
[190]

Yale University Press, 2020

Katie Trumpener and Tim Barringer.On the Viewing Platform: The Panorama Between Canvas and Screen. Yale University Press, 2020

2020
[191]

Multiframe image restoration and registration.Advance Computer Visual and Image Processing, 1:317–339, 1984

R Tsai. Multiframe image restoration and registration.Advance Computer Visual and Image Processing, 1:317–339, 1984

1984
[192]

The interpretation of structure from motion.Proceedings of the Royal Society of London

Shimon Ullman. The interpretation of structure from motion.Proceedings of the Royal Society of London. Series B. Biological Sciences, 203(1153):405–426, 1979

1979
[193]

Demon: Depth and motion net- work for learning monocular stereo

Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox. Demon: Depth and motion net- work for learning monocular stereo. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 5038–5047, 2017

2017
[194]

DeMoN: Depth and motion network for learning monocular stereo

Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox. DeMoN: Depth and motion network for learning monocular stereo. 2017

2017
[195]

Edge-based color con- stancy.IEEE Transactions on image processing, 16(9):2207–2214, 2007

Joost Van De Weijer, Theo Gevers, and Arjan Gijsenij. Edge-based color con- stancy.IEEE Transactions on image processing, 16(9):2207–2214, 2007. 152

2007
[196]

Piecewise rigid scene flow

Christoph Vogel, Konrad Schindler, and Stefan Roth. Piecewise rigid scene flow. InProceedings of the IEEE International Conference on Computer Vision, pages 1377–1384, 2013

2013
[197]

Synthetic depth-of-field with a single-camera mobile phone.ACM Transactions on Graphics (ToG), 37(4):1–13, 2018

Neal Wadhwa, Rahul Garg, David E Jacobs, Bryan E Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T Barron, Yael Pritch, and Marc Levoy. Synthetic depth-of-field with a single-camera mobile phone.ACM Transactions on Graphics (ToG), 37(4):1–13, 2018

2018
[198]

Deeplens: shallow depth of field from a single image.arXiv preprint arXiv:1810.08100, 2018

Lijun Wang, Xiaohui Shen, Jianming Zhang, Oliver Wang, Zhe Lin, Chih-Yao Hsieh, Sarah Kong, and Huchuan Lu. Deeplens: shallow depth of field from a single image.arXiv preprint arXiv:1810.08100, 2018

2018 arXiv
[199]

Nerf–: Neural radiance fields without known camera parameters.arXiv preprint arXiv:2102.07064, 2021

Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. Nerf–: Neural radiance fields without known camera parameters.arXiv preprint arXiv:2102.07064, 2021

2021 arXiv
[200]

Low-divergence high-power vcsel arrays for lidar application

Mial E Warren, David Podva, Preethi Dacha, Matthew K Block, Christopher J Helms, John Maynard, and Richard F Carson. Low-divergence high-power vcsel arrays for lidar application. InVertical-Cavity Surface-Emitting Lasers XXII, volume 10552, page 105520E. International Society ...

2018
[201]

Single image reflection removal exploiting misaligned training data and network en- hancements

Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, and Hua Huang. Single image reflection removal exploiting misaligned training data and network en- hancements. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8178–8187, 2019

2019
[202]

Universal discrete denoising: Known channel.IEEE Transactions on Information Theory, 51(1):5–28, 2005

Tsachy Weissman, Erik Ordentlich, Gadiel Seroussi, Sergio Verd´ u, and Marcelo J Weinberger. Universal discrete denoising: Known channel.IEEE Transactions on Information Theory, 51(1):5–28, 2005

2005
[203]

High performance imaging using large camera arrays

Bennett Wilburn, Neel Joshi, Vaibhav Vaish, Eino-Ville Talvala, Emilio An- tunez, Adam Barth, Andrew Adams, Mark Horowitz, and Marc Levoy. High performance imaging using large camera arrays. InACM siggraph 2005 papers, pages 765–776. 2005

2005
[204]

Handheld multi-frame super-resolution.ACM Transactions on Graphics (TOG), 38(4):1– 18, 2019

Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar. Handheld multi-frame super-resolution.ACM Transactions on Graphics (TOG), 38(4):1– 18, 2019

2019
[205]

Neural lens modeling

Wenqi Xian, Aljaˇ z Boˇ ziˇ c, Noah Snavely, and Christoph Lassner. Neural lens modeling. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8435–8445, 2023

2023
[206]

Foreground-aware image inpainting

Wei Xiong, Jiahui Yu, Zhe Lin, Jimei Yang, Xin Lu, Connelly Barnes, and Jiebo Luo. Foreground-aware image inpainting. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5840–5848, 2019. 153

2019
[207]

Depth from focusing and defocusing

Yalin Xiong and Steven A Shafer. Depth from focusing and defocusing. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 68–73. IEEE, 1993

1993
[208]

From shading to local shape.IEEE transactions on pattern analysis and machine intelligence, 37(1):67–79, 2014

Ying Xiong, Ayan Chakrabarti, Ronen Basri, Steven J Gortler, David W Ja- cobs, and Todd Zickler. From shading to local shape.IEEE transactions on pattern analysis and machine intelligence, 37(1):67–79, 2014

2014
[209]

A com- putational approach for obstruction-free photography.ACM Transactions on Graphics (TOG), 34(4):1–11, 2015

Tianfan Xue, Michael Rubinstein, Ce Liu, and William T Freeman. A com- putational approach for obstruction-free photography.ACM Transactions on Graphics (TOG), 34(4):1–11, 2015

2015
[210]

Polynomial neural fields for subband decomposition and manipulation.Advances in Neural Information Processing Systems, 35:4401–4415, 2022

Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan Bar- ron, Thomas Funkhouser, Bharath Hariharan, and Serge Belongie. Polynomial neural fields for subband decomposition and manipulation.Advances in Neural Information Processing Systems, 35:4401–4415, 2022

2022
[211]

S3net: A single stream structure for depth guided image relighting

Hao-Hsiang Yang, Wei-Ting Chen, and Sy-Yen Kuo. S3net: A single stream structure for depth guided image relighting. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 276–283, 2021

2021
[212]

Rgbd object tracking: An in-depth review.arXiv preprint arXiv:2203.14134, 2022

Jinyu Yang, Zhe Li, Song Yan, Feng Zheng, Aleˇ s Leonardis, Joni-Kristian K¨ am¨ ar¨ ainen, and Ling Shao. Rgbd object tracking: An in-depth review.arXiv preprint arXiv:2203.14134, 2022

2022 arXiv
[213]

Deformable sprites for unsupervised video decomposition

Vickie Ye, Zhengqi Li, Richard Tucker, Angjoo Kanazawa, and Noah Snavely. Deformable sprites for unsupervised video decomposition. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2657–2666, 2022

2022
[214]

Miniaturized color-sensitive photodetectors with hybrid silicon-aluminum nanostructures.Science Advances, 8(50):eadd3868, 2022

Jaeyun Yoon, Sang-Hoon Kim, Zhiyuan Wang, Junho Lee, Chuanhong Chen, Andrea Al` u, and Junsuk Rho. Miniaturized color-sensitive photodetectors with hybrid silicon-aluminum nanostructures.Science Advances, 8(50):eadd3868, 2022

2022
[215]

Plenoctrees for real-time rendering of neural radiance fields

Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoctrees for real-time rendering of neural radiance fields. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752– 5761, 2021

2021
[216]

pixelnerf: Neural radiance fields from one or few images

Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 4578–4587, 2021

2021
[217]

3d reconstruction from accidental motion

Fisher Yu and David Gallup. 3d reconstruction from accidental motion. InPro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3986–3993, 2014. 154

2014
[218]

As- projective-as-possible image stitching with moving dlt

Julio Zaragoza, Tat-Jun Chin, Michael S Brown, and David Suter. As- projective-as-possible image stitching with moving dlt. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 2339–2346, 2013

2013
[219]

Squaring the circle in panoramas

Lihi Zelnik-Manor, Gabriele Peters, and Pietro Perona. Squaring the circle in panoramas. InTenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1, volume 2, pages 1292–1299. IEEE, 2005

2005
[220]

Parallax-tolerant image stitching

Fan Zhang and Feng Liu. Parallax-tolerant image stitching. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3262–3269, 2014

2014
[221]

High-speed 3d shape measurement with structured light methods: A review.Optics and Lasers in Engineering, 106:119–131, 2018

Song Zhang. High-speed 3d shape measurement with structured light methods: A review.Optics and Lasers in Engineering, 106:119–131, 2018

2018
[222]

Streaming quanta sensors for online, high-performance imaging and vision.arXiv preprint arXiv:2406.00859, 2024

Tianyi Zhang, Matthew Dutson, Vivek Boominathan, Mohit Gupta, and Ashok Veeraraghavan. Streaming quanta sensors for online, high-performance imaging and vision.arXiv preprint arXiv:2406.00859, 2024

2024 arXiv
[223]

Lay- ered depth panoramas

Ke Colin Zheng, Sing Bing Kang, Michael F Cohen, and Richard Szeliski. Lay- ered depth panoramas. In2007 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8. IEEE, 2007

2007
[224]

Occlusion-free scene recovery via neural radiance fields

Chengxuan Zhu, Renjie Wan, Yunkai Tang, and Boxin Shi. Occlusion-free scene recovery via neural radiance fields. 2023

2023
[225]

State of the art on 3d reconstruction with rgb-d cameras

Michael Zollh¨ ofer, Patrick Stotko, Andreas G¨ orlitz, Christian Theobalt, Matthias Nießner, Reinhard Klein, and Andreas Kolb. State of the art on 3d reconstruction with rgb-d cameras. InComputer graphics forum, volume 37, pages 625–652. Wiley Online Library, 2018

2018
[226]

Seamless image stitching by minimizing false edges.IEEE transactions on image processing, 15(4):969–977, 2006

Assaf Zomet, Anat Levin, Shmuel Peleg, and Yair Weiss. Seamless image stitching by minimizing false edges.IEEE transactions on image processing, 15(4):969–977, 2006. 155

2006

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Defocus deblurring using dual-pixel data

Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. InEuropean Conference on Computer Vision, pages 111–126. Springer, 2020

2020

[2] [2]

De-fencing and multi-focus fusion using markov random field and image inpainting.IEEE Access, 10:35992–36005, 2022

Hannan Adeel, Muhammad Mohsin Riaz, and Syed Sohaib Ali. De-fencing and multi-focus fusion using markov random field and image inpainting.IEEE Access, 10:35992–36005, 2022

2022

[3] [3]

Pyramid methods in image processing.RCA engineer, 29(6):33– 41, 1984

Edward H Adelson, Charles H Anderson, James R Bergen, Peter J Burt, and Joan M Ogden. Pyramid methods in image processing.RCA engineer, 29(6):33– 41, 1984

1984

[4] [4]

Interactive digital photomontage

Aseem Agarwala, Mira Dontcheva, Maneesh Agrawala, Steven Drucker, Alex Colburn, Brian Curless, David Salesin, and Michael Cohen. Interactive digital photomontage. InACM SIGGRAPH 2004 Papers, pages 294–302. 2004

2004

[5] [5]

Panoramic video tex- tures

Aseem Agarwala, Ke Colin Zheng, Chris Pal, Maneesh Agrawala, Michael Co- hen, Brian Curless, David Salesin, and Richard Szeliski. Panoramic video tex- tures. InACM SIGGRAPH 2005 Papers, pages 821–827. 2005

2005

[6] [6]

Nonrigid structure from motion in trajectory space.Advances in neural information processing systems, 21, 2008

Ijaz Akhter, Yaser Sheikh, Sohaib Khan, and Takeo Kanade. Nonrigid structure from motion in trajectory space.Advances in neural information processing systems, 21, 2008

2008

[7] [7]

Video from stills: Lensless imaging with rolling shutter

Nick Antipa, Patrick Oare, Emrah Bostan, Ren Ng, and Laura Waller. Video from stills: Lensless imaging with rolling shutter. In2019 IEEE International Conference on Computational Photography (ICCP), pages 1–8. IEEE, 2019

2019

[8] [8]

Learning neural light fields with ray-space embedding

Benjamin Attal, Jia-Bin Huang, Michael Zollh¨ ofer, Johannes Kopf, and Changil Kim. Learning neural light fields with ray-space embedding. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19819–19829, 2022

2022