Pith. sign in

REVIEW 4 major objections 5 minor 183 references

A new rendering pipeline converts multiplane images into random-phase holograms, matching primitive-based CGH quality while running up to 250,000× faster.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

A new CGH pipeline converts multiplane-image stacks into random-phase holograms with wave-optics alpha compositing, matching Gaussian-based hologram quality while being orders of magnitude faster.

T0 review reviewed 2026-08-01 challenge →

load-bearing objection A plausible and useful pipeline paper: MPI-to-hologram via random-phase wave compositing, with a credible speedup claim and decent validation, but the load-bearing compositing law is adopted without derivation or error bounds and the headline speedup excludes upstream costs. the 4 major comments →

arxiv 2607.19731 v1 pith:3EH7UPDO submitted 2026-07-22 cs.GR cs.ETcs.MMeess.IVeess.SP

Fast Wave-optics Rendering of Multiplane Images for 3D Holographic Displays

Brian Chao , Dario Seyb , Nathan Matsuda , Oliver Cossairt , Yang Zhou , Douglas Lanman , Gordon Wetzstein , Grace Kuo
show 1 more author
Changwon Jang
This is my paper
classification cs.GR cs.ETcs.MMeess.IVeess.SP
keywords multiplane imagescomputer-generated holographyrandom-phase hologramswave-optics renderingangular spectrum methodtime multiplexinglight fieldfocal stack
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

Multiplane images (MPIs) are a layered 3D representation—a stack of RGBA images at discrete depths—that neural rendering has made easy to acquire, yet until now they have been viewed as 2D pictures. This paper proposes a wave-optics rendering pipeline that treats each MPI layer as a random-phase amplitude mask and sequentially propagates and composites the layers into a complex hologram at the display plane. The central claim is that this MPI-based CGH achieves the image quality of primitive-based computer-generated holography (which must composite millions of individual Gaussian or polygon wavefronts) while running up to 250,000× faster, and that it avoids the defocus and occlusion artifacts of RGBD-based layer CGH. This matters because holographic near-eye displays need an efficient path from casually captured or neural-rendered scene content to the patterns on a spatial light modulator, and MPIs are a compact, readily available intermediate format for front-facing scenes. The paper supports the claim with simulated focal stacks and light fields, benchtop captures on a phase-only SLM, and a demonstration with neural-network-predicted MPIs.

Core claim

At the paper's core is the recursive compositing equation u_{i-1} = P( √(1−α_i) u_i + √c_i √α_i e^{iφ_i}; Δz ), which takes the accumulated complex field from the far side of the stack, propagates it across the inter-layer gap with the angular spectrum method, and blends in the current layer's color weighted by the square root of its opacity and multiplied by a layer-specific random phase. Iterating from the backmost to the frontmost layer produces the full complex wavefront at the SLM plane. Repeated with different random phase maps and time-averaged, the pipeline reconstructs 3D focal stacks and 4D light fields with natural defocus, parallax, and occlusion. The paper's discovery is that a

What carries the argument

The load-bearing object is the sequential wavefront compositing rule in Eq. 2, adopted from the authors' earlier random-phase wave-splatting work. It defines how the accumulated field from deeper layers is attenuated by the current layer's transmittance √(1−α_i) and amplitude-weighted by √α_i with a random phase e^{iφ_i} before both are propagated by the angular spectrum method across the inter-layer gap. The rule is what turns an RGBA stack into a complex hologram in a single forward pass, and its correctness is what makes the natural-defocus, occlusion, and RPWS-parity results follow. The accompanying time-multiplexing rule (Eq. 3) averages intensities over T independent random-phase rende

Load-bearing premise

Everything rests on Eq. 2: that a partially transparent MPI layer behaves in wave optics as an amplitude mask with transmittance √α carrying a random phase—a law taken from earlier work without derivation or direct physical measurement in this paper.

What would settle it

Render a scene with a sharp occlusion boundary as both an MPI hologram and a primitive-based reference hologram, then capture focal slices at several depths on a benchtop display and compare the intensity profile across the edge; visible ringing or light leakage in the MPI case but not in the reference would refute the compositing law.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Scenes already represented as MPIs—whether from optimized splats, stereo networks, or single-image predictors—can be turned into holograms without per-scene optimization.
  • Runtime scales about linearly with the number of depth planes, and perceptual quality saturates near 30 layers, making a ~30-layer MPI a practical operating point for interactive displays.
  • Time multiplexing of independent random-phase renders reduces speckle contrast and expands the eyebox, improving space–bandwidth product utilization.
  • MPI holograms eliminate the halo, light-leakage, and dark-border occlusion artifacts that RGBD-based layer CGH exhibits at depth transitions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The paper's quantitative MPI evaluations are built mostly from binning optimized Gaussian splats (Eq. 4), so the claim that the pipeline is agnostic to MPI acquisition rests on a single qualitative neural-network demo; a systematic benchmark with predicted MPIs would directly test the motivating use case.
  • If Eq. 2 holds as a physical model, the same compositing scheme should transfer to other layered representations—layered depth images, alpha-matted RGBD, or learned layered radiance fields—with the same cost model, but the paper does not test these extensions.
  • Because the per-layer cost is essentially two Fourier transforms (propagation and composite), pairing the pipeline with a neural MPI predictor that emits 16–30 planes is the most direct route to real-time CGH; the paper identifies this direction but does not demonstrate it.
  • The paper inherits a known weakness of random-phase holograms—reduced image contrast—and mentions learning-based calibration as a remedy, but offers no quantitative measurement of the contrast cost or eyebox gain as T varies; a characterization experiment would help system designers choose T.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a wave-optics CGH pipeline that converts multiplane images (MPIs) into random-phase complex holograms via recursive back-to-front propagation and compositing (Eq. 2), with optional time multiplexing (Eq. 3). The claimed contributions are (1) an efficient direct CGH algorithm for MPIs that achieves up to 250,000× speedup over the primitive-based RPWS baseline while maintaining comparable reconstructed focal-stack and light-field quality, and (2) a detailed evaluation against RGBD layer-based and Gaussian-based methods. The method is validated on synthetic and captured benchtop results, including a demonstration with a neural-network MPI predictor (Stereo Magnification). The central claim is that MPI-based CGH offers the speed of RGBD methods and the quality of Gaussian primitive methods.

Significance. If validated, this is a significant advance: it links readily available neural-rendering representations (MPIs) to holographic displays, potentially enabling interactive CGH from casually captured scenes. The paper is clearly written and contains reproducible elements (source code/to-be-released datasets, PyTorch implementations, benchtop captures). The experimental captures in Fig. 8 are noteworthy, and the inclusion of a neural-MPI predictor in Fig. 6 strengthens the claim of representation agnosticism. However, the quantitative support for the headline speed/quality tradeoff is not fully rigorous, and the physical model underlying the recursion is imported from prior work without derivation or error analysis in this manuscript.

major comments (4)
  1. [Fig. 3 and Sec. 4.2.1] The recursive compositing law u_{i-1} = P( sqrt(1−α_i) u_i + sqrt(c_i) sqrt(α_i) e^{i φ_i}; Δz ) is the physical core of the paper, yet it is adopted from “recently proposed” work [Chao et al. 2025] with no derivation, no statement of the domain of validity for semi-transparent MPI planes, and no error bound. The paper itself acknowledges in Sec. 5 that “random phase leads to lower contrast” but does not quantify this or provide a contrast/speckle analysis vs. T or α. Since the claims of natural defocus, occlusion, and parity with RPWS all rest on this law, the paper should either derive it directly from wave optics or provide a quantitative validation (e.g., comparing against brute-force wave-propagation through a discretized volume for representative scenes, or bounding the interference error for finite T). As written, the physical correctness of the central quality claim is not self-c
  2. [Fig. 3 and Sec. 4.2.1] The headline 250,000× speedup is computed from the CGH propagation/compositing runtime only. The time to extract MPIs from Gaussian splats using Eq. 4, and the SGD-based phase-only encoding step (Sec. 4.1.4), are excluded. If the comparison is intended to reflect end-to-end hologram synthesis from a scene representation, the preprocessing costs should be included or explicitly excluded with a clear statement. The paper should report total pipeline times for a representative scene for all methods, so the reader can assess the practical speedup.
  3. [Sec. 4.1.2] Quantitative metrics (PSNR, SSIM) are reported only as averages over “selected scenes,” without per-scene values, standard deviations, or statistical significance tests. The claim that MPI “matches RPWS in SSIM” is not supported if the difference is within measurement noise. Please provide a per-scene breakdown (e.g., table or box plot) and state the number of scenes and selection criteria. This is essential to substantiate the core quality parity claim.
  4. [Sec. 4.1.2] The evaluation uses MPIs extracted from pre-optimized Gaussian splats via Eq. 4, not MPIs produced by the neural predictors that motivate the work. Fig. 6 shows only a qualitative example with Stereo Magnification. The paper should either provide quantitative results on neural-network MPIs (even a few scenes) or discuss explicitly how the Gaussian-splat-derived MPIs may differ in opacity/color statistics and therefore limit the generalization of the quality claims.
minor comments (5)
  1. [General] Typo: “addtionally” should be “additially” or “additionally.”
  2. [Abstract and Fig. 3] The 250,000× speedup corresponds to the lowest layer count (4) and should be reported together with the scaling: at 512 layers the speedup is far smaller. Please clarify in the abstract or the figure caption that this is a best-case value.
  3. [Sec. 4.1.4] The phase-only encoding step (SGD with complex loss) is an additional cost that is common to all compared methods. Please state explicitly that the reported speedup applies to complex-valued hologram generation, not to the phase-encoding step.
  4. [Sec. 4.2.1] The paper fixes T=24 for time multiplexing without an ablation. Since the random-phase compositing law converges to the target intensity only in expectation, please provide a brief study of the effect of T on image quality and runtime, or at least a sentence justifying the choice.
  5. [Fig. 3 caption] The caption states “matching RPWS in SSIM,” but the plotted MPI SSIM appears slightly lower than RPWS. Clarify whether “matching” is meant perceptually or statistically, and adjust the wording accordingly.

Circularity Check

1 steps flagged

Core wave-compositing law is adopted via a load-bearing self-citation, but the pipeline is otherwise externally validated.

specific steps
  1. self citation load bearing [Section 3.2, Eq. (2)]
    "we propagate the resulting wavefront across the inter-layer gap Δz=z_{i−1}−z_i to the subsequent plane i−1 using the angular spectrum method [Goodman 2005] and composite them using a recently proposed wave optics alpha blending formulation for random-phase wavefronts [Chao et al. 2025]: u^{(t)}_{i−1}(x)=P( sqrt(1−α_i(x)) u_i(x)+ sqrt(c_i) sqrt(α_i(x)) e^{iφ^{(t)}_i(x)}; Δz )"

    Eq. (2) is the only mechanism converting MPI alpha/color into holograms, so the paper's central claims of natural defocus, occlusion, and parity with RPWS stand or fall on this law. The paper gives no derivation, contrast bound, or speckle analysis for the sqrt(1−α)/sqrt(α) split; the only justification offered is a citation to the authors' own RPWS paper (Chao et al. 2025). The law is constructed so that time-averaged intensity reproduces standard alpha compositing, so the quality comparison is partly inherited from the same compositing target used to build the inputs; in any case, the physical validity is imported from a same-group reference rather than derived or independently verified here.

full rationale

The paper contains no parameter fitted to the target result: layer counts, random phase masks, and T=24 are fixed, and the experimental focal stacks/light fields are captured, not solved for. The speedup claim is structural (a handful of plane propagations vs millions of primitives) and credible. The pipeline is also shown to work with an off-the-shelf neural MPI predictor (Stereo Magnification, Zhou et al. 2018) and on public Mip-NeRF 360 and NeRF Blender scenes, which is external grounding. The main circularity concern is Eq. (2): it is adopted from the authors' prior RPWS work via self-citation and is load-bearing for every physical-quality claim, with no derivation or error bound in the present text. That justifies a moderate score, but not a higher one because the central contribution (MPI-to-hologram pipeline plus time multiplexing/evaluation) has independent empirical content and the comparison baselines are externally defined.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The central claim rests on standard wave propagation, a borrowed random-phase compositing law, an assumption that T-frame averaging suppresses speckle, and an ad-hoc validation proxy (MPIs extracted by binning Gaussian splats). No new physical entities are introduced; the main free parameters are algorithmic choices (layer count, frame count, propagation distance, depth binning).

free parameters (4)
  • MPI layer count L = 4, 64, 512 (ablations); 4 layers used for the 250,000x speedup claim
    The number of depth planes is user-chosen; it controls the speed/quality tradeoff and directly determines the headline speedup magnitude.
  • Time-multiplex frame count T = 24
    Chosen for all experiments; affects speckle suppression and runtime, but no T sweep is reported.
  • Phase-only encoding propagation distance = 4 cm
    Fixed SGD target distance in the experimental setup; display-dependent choice.
  • Uniform depth partitioning (z_min, z_max) = scene-dependent
    MPIs are extracted by binding Gaussian splats to uniformly spaced depth planes within the scene's depth range; this binning choice affects the resulting MPI quality.
axioms (4)
  • standard math Angular spectrum method (Goodman 2005) correctly models propagation between parallel planes in the MPI stack.
    Invoked in Eq. 2 via the P operator; a standard optics result that the paper does not re-derive.
  • domain assumption The random-phase wave-optics alpha compositing rule in Eq. 2 is a valid model of semi-transparent layers in coherent CGH.
    Adopted from the authors' own RPWS paper via citation; it is the load-bearing physical model for all quality claims.
  • domain assumption T-frame intensity averaging of fully random-phase holograms approximates the perceived image and suppresses speckle in the eyebox.
    Used in Eq. 3; the paper notes contrast loss from random phase but gives no quantitative bound.
  • ad hoc to paper MPIs extracted from Gaussian splats via Eq. 4 are representative of MPIs produced by neural predictors.
    Most validation uses Gaussian-binned MPIs, while the motivation relies on fast neural MPI generation; only one qualitative demo uses a neural MPI predictor.

reviewed 2026-08-01 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Fast Wave-optics Rendering of Multiplane Images for 3D Holographic Displays." pith.science (2026). https://pith.science/paper/3EH7UPDO

@misc{pith2026260719731,
  author       = {Pith},
  title        = {Pith review of: Fast Wave-optics Rendering of Multiplane Images for 3D Holographic Displays},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3EH7UPDO}},
  note         = {Machine review of arXiv:2607.19731}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Recent advances in neural rendering have unlocked unprecedented capabilities in 3D reconstruction and novel view synthesis, giving rise to applications such as virtual fly-throughs of a 3D scene reconstructed from a set of sparse, casually captured images. However, these renderings are viewed on a computer screen or conventional VR headsets as 2D images, greatly limiting the perceptual realism and immersiveness of such experiences. The rapid development in novel 3D scene representations calls for dedicated rendering algorithms that convert these readily-available 3D contents into formats that are compatible with emerging 3D display technologies, such as holographic displays. In this paper, we propose a wave-optics rendering pipeline that works with multiplane images (MPIs) for efficient and high-quality hologram synthesis. Our MPI-based computer-generated holography algorithm greatly outperforms state-of-the-art primitive-based CGH algorithms in terms of runtime, achieving speedups up to 250,000x while achieving comparable image quality, and significantly outperforms conventional layer-based CGH algorithms in terms of image quality. We validate our method extensively on a wide variety of 3D scene datasets both in simulation and through experimentally captured results, showing exceptional 3D focal stack and 4D light field reconstruction performance without sacrificing efficiency.

Figures

Figures reproduced from arXiv: 2607.19731 by Brian Chao, Changwon Jang, Dario Seyb, Douglas Lanman, Gordon Wetzstein, Grace Kuo, Nathan Matsuda, Oliver Cossairt, Yang Zhou.

Figure 1
Figure 1. Figure 1: We propose a wave-optics rendering pipeline for multiplane images (MPIs) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The MPI CGH pipeline. Our MPI-based CGH pipeline consists of 4 steps. We start with a set of (a) camera frustum-aligned multiplane images defined by Eq. 1, and (b) transform them into complex wavefronts aligned with the SLM plane by applying a random phase map to each RGBA image slice. The complex MPI wavefronts are (c) sequentially propagated and composited at each MPI depth plane using our novel wave com… view at source ↗
Figure 3
Figure 3. Figure 3: Runtime and image quality analysis of different CGH algorithms. We compare runtime and light field reconstruction quality across CGH algorithms with varying numbers of depth planes for RGBD- and MPI-based methods. We report average metrics on holograms generated from selected scenes of the Mip-NeRF 360 [Barron et al. 2022] dataset. RPWS achieves the highest image fidelity but is computationally expensive, … view at source ↗
Figure 4
Figure 4. Figure 4: Defocus and occlusion behaviours of different CGH algorithms at depth transitions. We show zoom-in crops of 3D focal stacks and 4D light fields reconstructed using different CGH algorithms. RPWS, a state-of-the-art primitives-based CGH method, produces the most natural defocus blur and accurate occlusion behavior. In contrast, RGBD holograms exhibit light leakage and halo artifacts in defocus regions, as w… view at source ↗
Figure 5
Figure 5. Figure 5: Experimentally captured light field parallax at different focused depths. We capture light field parallax at different depth planes by translating both the pupil horizontally at the Fourier plane and the camera along the optical axis. As expected, the parallax shift of objects increases as they move farther away from the focused depth, as seen from the red zoom-in crops for the front-focus results, and the… view at source ↗
Figure 6
Figure 6. Figure 6: MPI-based CGH works with MPIs produced by off-the-shelf neural networks. We use MPIs generated by Stereo Magnification [Zhou et al. 2018], a representative neural-network–based MPI predictor, for holo￾gram synthesis. Our MPI-based CGH algorithm is agnostic to the MPI gen￾eration process and reconstructs accurate 3D focal stacks. plane. We also capture the parallax effects at different focal planes (rear, m… view at source ↗
Figure 7
Figure 7. Figure 7: Simulated 3D focal stacks and 4D light fields of holograms generated using different CGH algorithms. RPWS achieves state-the-of-art focal stack and light field reconstruction quality among primitive-based CGH methods. RGBD holograms, despite being efficient to render, suffer from light leakage and halo artifacts in the reconstructed focal stacks and incorrect occlusion in the reconstructed light fields at … view at source ↗
Figure 8
Figure 8. Figure 8: Experimentally captured 3D focal stacks and 4D light fields of holograms generated using different CGH algorithms. We capture 3D focal stacks and horizontal parallax results of holograms generated using different CGH algorithms on our benchtop holographic display setup. The trend in the captured results well match the simulated experiments. Both RPWS and our MPI holograms reconstruct natural defocus blur a… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

183 extracted references · 4 canonical work pages

  1. [1]

    ACM SIGGRAPH 2023 Conference Proceedings , pages=

    Nerfstudio: A modular framework for neural radiance field development , author=. ACM SIGGRAPH 2023 Conference Proceedings , pages=

    2023

  2. [2]

    2023 , publisher=

    Physically based rendering: From theory to implementation , author=. 2023 , publisher=

    2023

  3. [3]

    ACM SIGGRAPH 2024 Conference Papers , pages=

    2d gaussian splatting for geometrically accurate radiance fields , author=. ACM SIGGRAPH 2024 Conference Papers , pages=

    2024

  4. [4]

    Applied optics , volume=

    Computer generated hologram from point cloud using graphics processor , author=. Applied optics , volume=. 2009 , publisher=

    2009

  5. [5]

    Applied Optics , volume=

    Fast occlusion processing for a polygon-based computer-generated hologram using the slice-by-slice silhouette method , author=. Applied Optics , volume=. 2018 , publisher=

    2018

  6. [6]

    Optics Express , volume=

    Occlusion handling using angular spectrum convolution in fully analytical mesh based computer generated hologram , author=. Optics Express , volume=. 2017 , publisher=

    2017

  7. [7]

    Photonics Research , volume=

    High-speed rendering pipeline for polygon-based holograms , author=. Photonics Research , volume=. 2023 , publisher=

    2023

  8. [8]

    Applied optics , volume=

    Fast polygon-based method for calculating computer-generated holograms in three-dimensional display , author=. Applied optics , volume=. 2013 , publisher=

    2013

  9. [9]

    Applied optics , volume=

    Rendering of specular curved objects in polygon-based computer holography , author=. Applied optics , volume=. 2017 , publisher=

    2017

  10. [10]

    Light: Science & Applications , volume=

    Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display , author=. Light: Science & Applications , volume=. 2022 , publisher=

    2022

  11. [11]

    Applied Optics , volume=

    Polygon-based computer-generated holography: a review of fundamentals and recent progress , author=. Applied Optics , volume=. 2022 , publisher=

    2022

  12. [12]

    Applied optics , volume=

    Extremely high-definition full-parallax computer-generated hologram created by the polygon-based method , author=. Applied optics , volume=. 2009 , publisher=

    2009

  13. [13]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Plenoxels: Radiance fields without neural networks , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  14. [14]

    Optics express , volume=

    Computer-generated holograms by multiple wavefront recording plane method with occlusion culling , author=. Optics express , volume=. 2015 , publisher=

    2015

  15. [15]

    Optics Express , volume=

    GPU-accelerated calculation of computer-generated holograms for line-drawn objects , author=. Optics Express , volume=. 2021 , publisher=

    2021

  16. [16]

    ACM SIGGRAPH 2022 Conference Proceedings , pages=

    Time-multiplexed neural holography: a flexible framework for holographic near-eye displays with fast heavily-quantized spatial light modulators , author=. ACM SIGGRAPH 2022 Conference Proceedings , pages=

    2022

  17. [17]

    Optics Express , volume=

    Modern hardware accelerated point based holography , author=. Optics Express , volume=. 2024 , publisher=

    2024

  18. [18]

    Optics express , volume=

    Silhouette method for hidden surface removal in computer holography and its acceleration using the switch-back technique , author=. Optics express , volume=. 2014 , publisher=

    2014

  19. [19]

    Optics Express , volume=

    Accelerated synthesis algorithm of polygon computer-generated holograms , author=. Optics Express , volume=. 2015 , publisher=

    2015

  20. [20]

    Optics Letters , volume=

    Point-polygon hybrid method for generating holograms , author=. Optics Letters , volume=. 2023 , publisher=

    2023

  21. [21]

    JOSA A , volume=

    Fast calculation method for optical diffraction on tilted planes by use of the angular spectrum of plane waves , author=. JOSA A , volume=. 2003 , publisher=

    2003

  22. [22]

    Journal of Information Display , volume=

    Recent progress in computer-generated holography for three-dimensional scenes , author=. Journal of Information Display , volume=. 2017 , publisher=

    2017

  23. [23]

    Applied optics , volume=

    Computer-generated holograms for three-dimensional surface objects with shade and texture , author=. Applied optics , volume=. 2005 , publisher=

    2005

  24. [24]

    Applied Optics , volume=

    Full-color digitized holography for large-scale holographic 3D imaging of physical and nonphysical objects , author=. Applied Optics , volume=. 2018 , publisher=

    2018

  25. [25]

    Optics express , volume=

    Occlusion culling for computer generated hologram based on ray-wavefront conversion , author=. Optics express , volume=. 2013 , publisher=

    2013

  26. [26]

    Applied optics , volume=

    Chen, Rick H-Y and Wilkinson, Timothy D , title=. Applied optics , volume=. 2009 , publisher=

    2009

  27. [27]

    Practical Holography XI and Holographic Materials III , volume=

    Occlusion processing and smooth surface shading for fully computed synthetic holography , author=. Practical Holography XI and Holographic Materials III , volume=. 1997 , organization=

    1997

  28. [28]

    The Visual Computer , volume=

    Detail-driven digital hologram generation , author=. The Visual Computer , volume=. 2010 , publisher=

    2010

  29. [29]

    Applied optics , volume=

    Hybrid approach for fast occlusion processing in computer-generated hologram calculation , author=. Applied optics , volume=. 2016 , publisher=

    2016

  30. [30]

    2020 , publisher=

    Introduction to Computer Holography: Creating Computer-Generated Holograms as the Ultimate 3D Image , author=. 2020 , publisher=

    2020

  31. [31]

    Optics Express , volume=

    Fast shadow casting algorithm in analytical polygon-based computer-generated holography , author=. Optics Express , volume=. 2023 , publisher=

    2023

  32. [32]

    Optics Express , volume=

    Calculation of reflectance distribution using angular spectrum convolution in mesh-based computer generated hologram , author=. Optics Express , volume=. 2016 , publisher=

    2016

  33. [33]

    Applied optics , volume=

    Mathematical modeling of triangle-mesh-modeled three-dimensional surface objects for digital holography , author=. Applied optics , volume=. 2008 , publisher=

    2008

  34. [34]

    Applied optics , volume=

    Computer generated holograms from three dimensional meshes using an analytic light transport model , author=. Applied optics , volume=. 2008 , publisher=

    2008

  35. [35]

    2005 , publisher=

    Introduction to Fourier optics , author=. 2005 , publisher=

    2005

  36. [36]

    ACM Transactions on Graphics (TOG) , volume=

    Neural point catacaustics for novel-view synthesis of reflections , author=. ACM Transactions on Graphics (TOG) , volume=. 2022 , publisher=

    2022

  37. [37]

    and Mildenhall, Ben and Verbin, Dor and Srinivasan, Pratul P

    Barron, Jonathan T. and Mildenhall, Ben and Verbin, Dor and Srinivasan, Pratul P. and Hedman, Peter , booktitle=

  38. [38]

    NeurIPS , year =

    3D Gaussian Splatting as Markov Chain Monte Carlo , author =. NeurIPS , year =

  39. [39]

    gsplat: An Open-Source Library for

    Vickie Ye and Ruilong Li and Justin Kerr and Matias Turkulainen and Brent Yi and Zhuoyang Pan and Otto Seiskari and Jianbo Ye and Jeffrey Hu and Matthew Tancik and Angjoo Kanazawa , year=. gsplat: An Open-Source Library for. arXiv , primaryClass=:2409.06765 , journal=

    Pith/arXiv arXiv

  40. [40]

    ACM Transactions on Graphics (TOG) , volume=

    Neural holography with camera-in-the-loop training , author=. ACM Transactions on Graphics (TOG) , volume=. 2020 , publisher=

    2020

  41. [41]

    JOSA A , volume=

    Hologram synthesis for photorealistic reconstruction , author=. JOSA A , volume=. 2008 , publisher=

    2008

  42. [42]

    Nature , volume=

    Towards real-time photorealistic 3D holography with deep neural networks , author=. Nature , volume=. 2021 , publisher=

    2021

  43. [43]

    ACM Transactions on Graphics (Tog) , volume=

    Holographic near-eye displays for virtual and augmented reality , author=. ACM Transactions on Graphics (Tog) , volume=. 2017 , publisher=

    2017

  44. [44]

    Applied optics , volume=

    Accurate phase-added stereogram to improve the coherent stereogram , author=. Applied optics , volume=. 2008 , publisher=

    2008

  45. [45]

    Applied optics , volume=

    Computer-generated hologram with occlusion effect using layer-based processing , author=. Applied optics , volume=. 2017 , publisher=

    2017

  46. [46]

    Practical Holography XIX: Materials and Applications , volume=

    Exact hidden-surface removal in digitally synthetic full-parallax holograms , author=. Practical Holography XIX: Materials and Applications , volume=. 2005 , organization=

    2005

  47. [47]

    JOSA A , volume=

    Diffusers in digital holography , author=. JOSA A , volume=. 1991 , publisher=

    1991

  48. [48]

    ACM Transactions on Graphics (TOG) , volume=

    Neural 3d holography: Learning accurate wave propagation models for 3d holographic virtual and augmented reality displays , author=. ACM Transactions on Graphics (TOG) , volume=. 2021 , publisher=

    2021

  49. [49]

    Light: Science & Applications , volume=

    End-to-end learning of 3d phase-only holograms for holographic display , author=. Light: Science & Applications , volume=. 2022 , publisher=

    2022

  50. [50]

    ACM Transactions on Graphics (TOG) , volume=

    Holographic near-eye displays based on overlap-add stereograms , author=. ACM Transactions on Graphics (TOG) , volume=. 2019 , publisher=

    2019

  51. [51]

    2022 , isbn =

    Kim, Jonghyun and Gopakumar, Manu and Choi, Suyeon and Peng, Yifan and Lopes, Ward and Wetzstein, Gordon , title =. 2022 , isbn =. doi:10.1145/3528233.3530739 , booktitle =

    arXiv 2022

  52. [52]

    Nature , pages=

    Full-colour 3D holographic augmented-reality displays with metasurface waveguides , author=. Nature , pages=. 2024 , publisher=

    2024

  53. [53]

    Nature Communications , volume=

    Waveguide holography for 3D augmented reality glasses , author=. Nature Communications , volume=. 2024 , publisher=

    2024

  54. [54]

    Optica , volume=

    Toward the next-generation VR/AR optics: a review of holographic near-eye displays from a human-centric perspective , author=. Optica , volume=. 2020 , publisher=

    2020

  55. [55]

    Computer Graphics Forum , volume=

    Advances in neural rendering , author=. Computer Graphics Forum , volume=. 2022 , organization=

    2022

  56. [56]

    Computer Graphics Forum , volume=

    State of the art on neural rendering , author=. Computer Graphics Forum , volume=. 2020 , organization=

    2020

  57. [57]

    arXiv preprint arXiv:2401.03890 , year=

    A survey on 3d gaussian splatting , author=. arXiv preprint arXiv:2401.03890 , year=

    Pith/arXiv arXiv

  58. [58]

    Journal of display technology , volume=

    State of the art in holographic displays: a survey , author=. Journal of display technology , volume=. 2010 , publisher=

    2010

  59. [59]

    Optics Express , volume=

    Roadmap on digital holography , author=. Optics Express , volume=. 2021 , publisher=

    2021

  60. [60]

    Xu, Qiangeng and Xu, Zexiang and Philip, Julien and Bi, Sai and Shu, Zhixin and Sunkavalli, Kalyan and Neumann, Ulrich , booktitle=

  61. [61]

    and Sevastopolsky, A

    Aliev, K. and Sevastopolsky, A. and Kolos, M. and Ulyanov, D. and Lempitsky, V. , title =. 2019 , eprint=

    2019

  62. [62]

    Kopanas, Georgios and Philip, Julien and Leimk \"u hler, Thomas and Drettakis, George. CGF. 2021

    2021

  63. [63]

    Sara Fridovich-Keil and Alex Yu and Matthew Tancik and Qinhong Chen and Benjamin Recht and Angjoo Kanazawa , year=

  64. [64]

    Alex Yu and Ruilong Li and Matthew Tancik and Hao Li and Ren Ng and Angjoo Kanazawa , year=

  65. [65]

    and Zhang, Z

    Hu, D. and Zhang, Z. and Hou, T. and Liu, T. and Fu, H. and Gong, M. , title =. ICCV , year =

  66. [66]

    and Pathak, Deepak , booktitle=

    Hu, Ronghang and Ravi, Nikhila and Berg, Alexander C. and Pathak, Deepak , booktitle=

  67. [67]

    SIGGRAPH Asia , year =

    Guo, Yuan-Chen and Cao, Yan-Pei and Wang, Chen and He, Yu and Shan, Ying and Zhang, Song-Hai , title =. SIGGRAPH Asia , year =

  68. [68]

    ACM TOG , volume =

    Lombardi, Stephen and Saragih, Jason and Simon, Tomas and Sheikh, Yaser , title =. ACM TOG , volume =

  69. [69]

    Chong Bao and Bangbang Yang and Zeng Junyi and Bao Hujun and Zhang Yinda and Cui Zhaopeng and Zhang Guofeng , booktitle=

  70. [70]

    and Ushiku, Y

    Kato, H. and Ushiku, Y. and Harada, T. , title =. CVPR , year =

  71. [71]

    and Tancik, Matthew and Barron, Jonathan T

    Mildenhall, Ben and Srinivasan, Pratul P. and Tancik, Matthew and Barron, Jonathan T. and Ramamoorthi, Ravi and Ng, Ren , booktitle=

  72. [72]

    ACM TOG , volume =

    M\"uller, Thomas and Evans, Alex and Schied, Christoph and Keller, Alexander , title =. ACM TOG , volume =

  73. [73]

    Advances in neural information processing systems , volume=

    Implicit neural representations with periodic activation functions , author=. Advances in neural information processing systems , volume=

  74. [74]

    NeurIPS , year=

    Sitzmann, Vincent and Zollh. NeurIPS , year=

  75. [75]

    ACM TOG , volume =

    Kerbl, Bernhard and Kopanas, Georgios and Leimk. ACM TOG , volume =

  76. [76]

    ICCV , year=

    Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement , author=. ICCV , year=

  77. [77]

    NeurIPS , year=

    NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction , author=. NeurIPS , year=

  78. [78]

    ACM TOG , month =

    Hanocka, Rana and Metzer, Gal and Giryes, Raja and Cohen-Or, Daniel , title =. ACM TOG , month =. 2020 , volume =

    2020

  79. [79]

    and Cline, Harvey E

    Lorensen, William E. and Cline, Harvey E. , title =. 1987 , booktitle =

    1987

  80. [80]

    SGP , year=

    Poisson Surface Reconstruction , author=. SGP , year=

Showing first 80 references.

This paper was first reviewed by deepseek-v4-flash on August 1, 2026.