Pith. sign in

REVIEW 3 major objections 5 minor 4 cited by

Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read The best next view is the one that currently renders worst, and a 2D image-quality rule can pick it 14–33× faster than 3D uncertainty methods.

desk verdict A practical reframing of active view selection as 2D IQA with consistent empirical gains; the central heuristic is unproven but the paper is honest about it, and it deserves a serious referee. read the letter →

arxiv 2506.19844 v1 pith:6YPUYCVJ submitted 2025-06-24 cs.CV

Zirui Wang , Yash Bhalgat , Ruining Li , Victor Adrian Prisacariu This is my paper · ORCID
classification cs.CV
keywords activeviewselectionimagequalityassessmentcross-referenceIQAnovelsynthesis3Dreconstructionnext-bestGaussianSplattingself-supervisedlearning
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

The paper tries to establish that active view selection—choosing the next camera pose to photograph when reconstructing a 3D scene—can be solved entirely in 2D image space. Its proposal: render the scene from every candidate viewpoint with the current reconstruction, predict how bad each rendering looks compared with already-captured photos, and add the view that looks worst. The authors argue this "boost where it struggles" rule matches the estimated-error-reduction idea from active learning, and they report that a cross-reference image quality model trained to predict SSIM maps outperforms prior 3D uncertainty- and information-gain-based methods on several benchmarks while running 14–33× faster. If true, view selection no longer needs specialized 3D representations, opening the door to a single lightweight, representation-agnostic selector for robots, SLAM, and AR/VR.

What carries the argument

The central object is a cross-reference image quality assessment network $f_\theta$ that maps a query rendering plus $K$ reference images from other viewpoints to a per-pixel SSIM prediction. It replaces the heavy DINOv2 backbone of the prior CrossScore model with RepViT for low latency, keeping a transformer-based cross-reference module that lets the query image attend to the reference views. The selection algorithm literally loops: optimize the current reconstruction, render all candidate poses, run $f_\theta$, add the pose with the lowest predicted SSIM, repeat. The machinery does the work of translating a 3D information-acquisition problem into a 2D perceptual-quality prediction problem, which is what buys representation agnosticism and speed.

What would settle it

Take a scene containing a strongly specular or mirror-like surface, run the proposed worst-SSIM selection for a fixed budget, and track actual reconstruction quality (e.g., PSNR on held-out views) after every added view. If the method repeatedly selects views aimed at the specular region and the held-out quality stalls or declines while errors elsewhere remain, the local-error-reduction premise is falsified; the same experiment with a matte-control scene should show steady improvement.

Watch

Extended reading notes

Core claim

On its own terms, the paper's claim is: a multi-view image quality assessment model can serve as the informativeness measure for active view selection, and the best next view is the one whose current rendering has the lowest predicted quality. Because the ground-truth image of a candidate view is unavailable at selection time, the model is trained to predict the SSIM map of a rendering relative to its ground truth, using other real views of the same scene as cross-reference context; that training is self-supervised, generated by fitting radiance fields and recording render-versus-truth SSIM. At selection time the model is applied feed-forward to rank candidates. The reported consequence is higher novel-view-synthesis quality than 3D-based selection, better geometric coverage in SfM and active SLAM, and a 14× speedup over FisherRF and 33× over its batched variant, with memory under half in the lightweight configuration.

Load-bearing premise

The whole method rests on the claim that a view's current rendering quality is a reliable proxy for how much that same view will improve the reconstruction when added; if a rendering looks bad for reasons extra photos of that viewpoint cannot fix, the selection rule can mislead.

Editorial extensions

If this is right

  • A single trained IQA model can drive view selection for 3DGS, NeRF, SDF, or voxel reconstructions without retraining or access to internal 3D state.
  • View-selection latency drops to roughly 0.5–1.2 seconds per choice on a 4090 GPU, making interactive and robotic acquisition feasible where 5–10 second delays previously blocked it.
  • Even plain no-reference IQA metrics (MANIQA, MUSIQ) are competitive with 3D uncertainty methods on novel view synthesis, suggesting rendering quality is a strong signal on its own.
  • Better view selection improves not just rendered image quality but geometric coverage—higher SCR and F-score in SfM and lower depth error in active SLAM—so downstream mapping tasks benefit directly.

Reading between the lines

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

  • A natural but untested extension is to use the same worst-rendering rule for other acquisition problems—depth completion, semantic mapping, or training data selection for generative models—wherever a candidate's future contribution is hard to model.
  • The local-error-reduction assumption predicts a specific failure mode: scenes with view-dependent effects (specularity, transparency, moving objects) may cause the method to keep revisiting a badly rendered region that no additional photo of the same viewpoint can repair; testing on such scenes would sharpen the boundary of the claim.
  • Combining a cheap 2D quality pre-rank with a 3D verification step for the top few candidates could recover robustness to view-dependent artifacts while keeping most of the speed advantage.
  • Because the training data come from radiance-field renderings of real scenes, the same self-supervised recipe could be re-run on a target domain (egocentric, underwater, aerial) to adapt the selector quickly.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes to reframe active view selection for novel view synthesis and 3D reconstruction as a 2D image quality assessment (IQA) problem. It trains a cross-reference IQA model, based on CrossScore, that predicts SSIM maps of a rendered image given a set of real reference views from the same scene, then uses the predicted quality as an inverse informativeness score: the candidate view whose current rendering has the lowest predicted SSIM is selected next. The method is tested on active NVS with 3D Gaussian Splatting on Mip-NeRF360, RealEstate10K, and MFR, on SfM coverage with MASt3R, on active SLAM with SplaTAM in Habitat-Gibson, and on an out-of-distribution egocentric ARIA-DTC subset. The authors report improved NVS quality, coverage, and depth error over FisherRF and over a range of NR-IQA baselines, while reducing view-selection latency by 14-33x and halving GPU memory relative to FisherRF. The paper's central claim is that view selection can be decoupled from the 3D representation and treated as a 2D cross-reference image quality assessment, with a large runtime advantage.

Significance. If the result holds, the paper offers a practically valuable simplification: active view selection can be performed with a lightweight 2D IQA network that operates on rendered images, independent of the underlying 3D representation. The cross-reference IQA idea, adapted from CrossScore, is a reasonable way to inject multi-view context into quality prediction, and the reported speedups (0.59s vs 8.34s per selection step) are relevant to robotics and AR/VR. The paper is also honest in showing that several NR-IQA baselines are surprisingly competitive, which strengthens the observation that 2D quality is a useful proxy. However, the central decision rule rests on an unproven equivalence between low predicted rendering quality at a viewpoint and the marginal reconstruction improvement obtained by adding that viewpoint, and the headline runtime claim is presented in a way that overstates the advantage. The experimental margins over FisherRF are small and reported without variance or significance tests, so the quantitative superiority is not yet firmly established.

major comments (3)
  1. [Sec. 3.2, decision rule] The load-bearing premise is the sentence: 'Assuming that each additional view locally reduces reconstruction error around its viewpoint, selecting the view with the highest error prediction (i.e., lowest SSIM score) aligns with the estimated error reduction framework [26].' This assumption is not justified formally or empirically. The network ftheta is trained to predict SSIM(render(g_w,p), ground-truth at p), but the quantity view selection actually requires is the marginal improvement in held-out reconstruction quality after adding p. These two quantities can diverge: a view can render poorly because of view-dependent effects, occlusions, or artifacts that adding that same view will not correct. I request a direct diagnostic: for a subset of scenes, compute the Spearman correlation between ftheta's predicted scores and the actual improvement in held-out PSNR/SSIM after adding each candidate view, and/or add an oracle ablation that selects views using ground-truth SSIM at candidate viewpoints. Such an experiment would separate the accuracy of the IQA predictor from the validity of the selection rule and is essential to support the central claim of the paper.
  2. [Abstract, Fig. 1, Table 5, Sec. 4.4] The headline '14-33x faster' compares per-selection-step wall time: FisherRF 8.34s vs Ours 0.59s gives 14x, and FisherRF4 19.70s vs Ours 0.59s gives 33x. This comparison is misleading because FisherRF4 selects four views per step. To reach the same 20-view budget, FisherRF4 requires five steps (about 98.5s total), whereas Ours requires sixteen steps (about 9.4s total), a factor of roughly 10x, not 33x. Similarly, the 14x figure is per-step, not total time to budget. The abstract's '14-33x faster' claim should be qualified by reporting either per-view selection time or total time for the full selection budget; the current wording overstates the speedup.
  3. [Tables 1, 2, 7, 8] The quantitative gains over FisherRF are small in absolute terms (mean PSNR 20.34 vs 20.97 on Mip-NeRF360; 18.86 vs 19.29 on RE10K), and no error bars, multiple-seed results, or significance tests are reported. Per-scene results in Table 7 show that on several scenes (Bicycle, Counter, Treehill) the PSNR difference is below 0.3 dB, and on the ARIA-DTC subset (Table 8) Ours is slightly worse than FisherRF (PSNR 20.85 vs 21.07, SSIM 0.79 vs 0.80). Without variance estimates or paired statistical tests, the claim of 'substantial quantitative improvements' is not robustly supported. Please report standard deviations over at least three seeds, and ideally paired tests across scenes.
minor comments (5)
  1. [Abstract] The abstract contains a typo: 'FisheRF' should be 'FisherRF'.
  2. [References] Reference [2] is the original Mip-NeRF paper (ICCV 2021), but the dataset used throughout is Mip-NeRF360; the proper citation is Barron et al., 'Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields', CVPR 2022.
  3. [Table 1] The row labeled 'Oracle FVS' is unclear: FVS (farthest view sampling) is a deterministic baseline, not an oracle. Please clarify whether this is farthest-point sampling using all 20 views or a different upper-bound procedure.
  4. [Sec. 3.1] The training described is not truly self-supervised: the network is supervised with SSIM maps computed from ground-truth images of the training scenes. Please rephrase as 'supervised with automatically generated pseudo-labels' or similar.
  5. [Sec. 4.4] The text states that FisherRF's 70 fps evaluation rate over about 200 candidate views leads to 5-8 seconds per selection, but 200/70 is about 2.9 seconds. Please clarify whether the 5-8 seconds includes additional overhead such as Hessian construction or retraining.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: evaluation is against held-out and out-of-distribution benchmarks, and the Sec. 3.2 'lowest SSIM = best next view' step is an explicit heuristic assumption, not a self-referential reduction.

full rationale

The paper's derivation chain is: train the cross-reference model f_theta on MFR radiance-field renderings to predict SSIM maps; at selection time, render candidate poses with the current Gaussian Splatting model and choose argmin_p f_theta(render(g_w,p), I_cur); then evaluate the final reconstruction with ground-truth PSNR/SSIM/LPIPS on held-out MFR videos, Mip-NeRF360, RE10K, and ARIA-DTC. The training target of f_theta is the SSIM of a current rendering against its ground-truth image, while the reported evaluation metric is the quality of the final reconstruction after views are added; these are different quantities, and no fitted parameter is reused as the evaluation score. The bridge between them, stated in Sec. 3.2 as 'Assuming that each additional view locally reduces reconstruction error around its viewpoint, selecting the view with the highest error prediction (i.e., lowest SSIM score) aligns with the estimated error reduction framework,' is an explicit assumption and the main correctness risk, but it is not an equation that reduces to its own inputs. The same-author CrossScore citation [43] is used as architectural inspiration and as the pretrained model for Ours-DINOv2, but the central empirical claim is assessed against external benchmarks and against NR-IQA baselines that do not rely on CrossScore, so the self-citation is not load-bearing. The ARIA-DTC supplementary result (Ours PSNR 20.85 vs FisherRF 21.07) is an acknowledged limitation, not evidence of circularity. No specific step can be quoted in which a prediction is equivalent by construction to its input.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The method is an empirical machine-learning approach; it introduces no hand-fitted physical constants or invented entities. The central claim rests on a few domain assumptions about the relationship between rendered-image quality and reconstruction informativeness, and about the generalization of the trained IQA model. The listed free parameters are hand-chosen hyperparameters that are not ablated.

free parameters (3)
  • Number of reference views K = 5
    Chosen by hand following CrossScore; the cross-reference model input depends on K and no sensitivity analysis is provided.
  • View-add schedule = [400, 900, 1500, 2200, ...] over 30k iterations
    Adapted from FisherRF; determines when the 16 new views are inserted during optimization. The paper uses this schedule without ablation, so the relative performance could depend on it.
  • IQA input resolution = 518x518
    Standard choice from CrossScore and the 2D baselines; not likely critical but is a hand-set constant that is not ablated.
assumptions (3)
  • domain assumption The predicted SSIM of a rendered candidate view is a reliable inverse proxy for the information gain of adding that view.
    Stated in Sec. 3.2: 'Assuming that each additional view locally reduces reconstruction error around its viewpoint'. This is the load-bearing premise of the method; it is empirically validated only indirectly.
  • domain assumption The distribution of rendering artifacts in the training data (MFR scenes with 3DGS, NeRF, and TensoRF renderings) is representative of artifacts in test scenes (Mip-NeRF360, RE10K, ARIA) and across representations.
    The IQA model is trained only on MFR (348 videos) and applied to other datasets; generalization is an empirical assumption, partially validated by the ARIA experiment.
  • domain assumption The cross-reference model can infer the quality of a novel-view rendering given K real reference images from the same scene.
    This is the core assumption of cross-reference IQA, inherited from CrossScore; the paper does not prove it but provides empirical evidence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment." pith.science (2026). https://pith.science/paper/6YPUYCVJ

@misc{pith2026250619844,
  author       = {Pith},
  title        = {Pith review of: Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6YPUYCVJ}},
  note         = {Machine review of arXiv:2506.19844}
}
read the original abstract

We tackle active view selection in novel view synthesis and 3D reconstruction. Existing methods like FisheRF and ActiveNeRF select the next best view by minimizing uncertainty or maximizing information gain in 3D, but they require specialized designs for different 3D representations and involve complex modelling in 3D space. Instead, we reframe this as a 2D image quality assessment (IQA) task, selecting views where current renderings have the lowest quality. Since ground-truth images for candidate views are unavailable, full-reference metrics like PSNR and SSIM are inapplicable, while no-reference metrics, such as MUSIQ and MANIQA, lack the essential multi-view context. Inspired by a recent cross-referencing quality framework CrossScore, we train a model to predict SSIM within a multi-view setup and use it to guide view selection. Our cross-reference IQA framework achieves substantial quantitative and qualitative improvements across standard benchmarks, while being agnostic to 3D representations, and runs 14-33 times faster than previous methods.

Figures

Figures reproduced from arXiv: 2506.19844 by the authors.

Figure 1
Figure 1. View selection time in seconds (↓) vs. NVS qual￾ity measured by PSNR (↑) on the Garden scene from Mip￾NeRF360 dataset. [2]. Our method achieves a 14× speedup over the state-of-the-art view selection method, FisherRF [10], and a 33× speedup over its batched variant, FisherRF4, while achieving improved NVS quality. Notably, several no-reference IQA-based approaches also emerge as strong baselines for this task. gests … view at source ↗
Figure 2
Figure 2. Method Overview. Our method consists of two main components. First, a lightweight cross-referencing (CR) image quality assessment (IQA) model (left) evaluates a rendered image by comparing it to multiple real images from different viewpoints of the same scene, generating a per-pixel quality map. This model is designed for multi-view novel view synthesis (NVS), where conventional metrics like PSNR and SSIM are inappl… view at source ↗
Figure 3
Figure 3. Example NVS results after running active view selection with Gaussian Splatting on three datasets: RealEstate10K, Map-free Relocalisation, and Mip-NeRF360. We compare our method with the state-of-the-art FisherRF model and present results from view selection pipelines using two strong no-reference IQA metrics: MANIQA and MUSIQ. IQA baselines: MANIQA and MUSIQ. Note that our method not only outperforms all pre￾vious … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visualization of Scene Coverage. We compare reconstruction errors for each view selection strategy. Errors are visualized as distances from each point in the “complete” point cloud to its nearest neighbor in the reconstruction produced by the subset of views selected b…
Figure 5
Figure 5. Figure 5: View selection time (left) and test split PSNR (right) on the Garden scene from the Mip-NeRF360 dataset. Our method provides the highest NVS quality whilst being 14× faster than the state-of-the-art model FisherRF and 33× faster than the batched version FisherRF4. Stro…
Figure 6
Figure 6. Figure 6: Images Captured from Egocentric Devices (ARIA) vs. Mobile Phones. Note that even after rectification, egocentric images differ in three key ways: 1) vignette effect, 2) lower expo￾sure, and 3) a wider field of view. eras. Key factors include the super-wide field of vie…

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FillGS: Filling Observation Gaps in 4D Gaussian Splatting via Viewpoint-Time Selection and Generative Refinement

    cs.CV 2026-07 conditional novelty 6.0 of 10

    FillGS actively selects spatiotemporal virtual viewpoints using rendering sensitivity and motion-aware observation density, then fine-tunes 4D Gaussian Splatting with reliability-masked generated images, improving spa...

  2. GO-PRE: Goal-Oriented Next-Best-View Selection via Predictive Rendering Entropy for Active 3D Reconstruction

    cs.CV 2026-07 conditional novelty 6.0 of 10

    GO-PRE proposes a next-best-view selection score that minimizes an upper bound on predictive rendering entropy over a user-specified target view manifold for 3D Gaussian Splatting.

  3. DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles

    cs.CV 2026-02 unverdicted novelty 6.0 of 10

    DAV-GSWT uses diffusion priors and active view sampling to synthesize high-fidelity Gaussian Splatting Wang Tiles from minimal observations while preserving visual quality and tile transitions.

  4. NI-Tex: Non-isometric Image-based Garment Texture Generation

    cs.CV 2025-11 conditional novelty 6.0 of 10

    A training framework that makes image-to-garment texture transfer robust to pose and topology mismatch, using simulated garment videos, AI image editing, and uncertainty-guided multi-view baking.

Reference graph

Works this paper leans on

49 extracted references · 43 canonical work pages · cited by 4 Pith papers

  1. [26]

    Roy, nicholas and mccallum, andrew

    Toward optimal active learning through monte carlo estima- tion of error reduction. Roy, nicholas and mccallum, andrew. In ICML, 2001. 2, 4

    work page 2001

  2. [1]

    Map-free visual relocalization: Metric pose relative to a single image

    Eduardo Arnold, Jamie Wynn, Sara Vicente, Guillermo Garcia-Hernando, ´Aron Monszpart, Victor Adrian Prisacariu, Daniyar Turmukhambetov, and Eric Brach- mann. Map-free visual relocalization: Metric pose relative to a single image. In ECCV, 2022. 5, 11

    work page 2022

  3. [2]

    Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields

    Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields. In ICCV, 2021. 1, 5, 11

    work page 2021

  4. [3]

    Generative novel view synthesis with 3d-aware diffusion models

    Eric R Chan, Koki Nagano, Matthew A Chan, Alexander W Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. Generative novel view synthesis with 3d-aware diffusion models. In ICCV, 2023. 1

    work page 2023

  5. [4]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In ECCV, 2022. 1, 2, 4

    work page 2022

  6. [5]

    Topiq: A top-down approach from semantics to distortions for image quality assessment

    Chaofeng Chen, Jiadi Mo, Jingwen Hou, Haoning Wu, Liang Liao, Wenxiu Sun, Qiong Yan, and Weisi Lin. Topiq: A top-down approach from semantics to distortions for image quality assessment. IEEE Transactions on Image Processing,

  7. [6]

    Pred-nbv: Prediction-guided next-best-view planning for 3d object reconstruction

    Harnaik Dhami, Vishnu D Sharma, and Pratap Tokekar. Pred-nbv: Prediction-guided next-best-view planning for 3d object reconstruction. In IROS, 2023. 2

    work page 2023

  8. [7]

    No-reference image quality assessment via transformers, rel- ative ranking, and self-consistency

    S Alireza Golestaneh, Saba Dadsetan, and Kris M Kitani. No-reference image quality assessment via transformers, rel- ative ranking, and self-consistency. In WACV, 2022. 5

    work page 2022

Show all 49 references
  1. [8]

    Lrm: Large reconstruction model for single image to 3d

    Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. In ICLR, 2024. 1

    2024

  2. [9]

    Farm3D: Learning articulated 3d animals by distilling 2d diffusion

    Tomas Jakab, Ruining Li, Shangzhe Wu, Christian Rup- precht, and Andrea Vedaldi. Farm3D: Learning articulated 3d animals by distilling 2d diffusion. In 3DV, 2024. 1

    2024

  3. [10]

    Fisherrf: Ac- tive view selection and mapping with radiance fields using fisher information

    Wen Jiang, Boshu Lei, and Kostas Daniilidis. Fisherrf: Ac- tive view selection and mapping with radiance fields using fisher information. In ECCV, 2024. 1, 2, 3, 4, 5, 8

    2024

  4. [11]

    Neu-nbv: Next best view planning using uncer- tainty estimation in image-based neural rendering

    Liren Jin, Xieyuanli Chen, Julius R ¨uckin, and Marija Popovi´c. Neu-nbv: Next best view planning using uncer- tainty estimation in image-based neural rendering. In IROS,

  5. [12]

    Musiq: Multi-scale image quality transformer

    Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. Musiq: Multi-scale image quality transformer. In ICCV, 2021. 5, 8

    2021

  6. [13]

    Splatam: Splat track & map 3d gaussians for dense rgb-d slam

    Nikhil Keetha, Jay Karhade, Krishna Murthy Jatavallabhula, Gengshan Yang, Sebastian Scherer, Deva Ramanan, and Jonathon Luiten. Splatam: Splat track & map 3d gaussians for dense rgb-d slam. In CVPR, 2024. 4, 8

    2024

  7. [14]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM TOG, 2023. 1, 2, 3, 4

    2023

  8. [15]

    Tanks and temples: Benchmarking large-scale scene reconstruction

    Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM TOG, 2017. 6

    2017

  9. [16]

    Ground- ing image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and J´erˆome Revaud. Ground- ing image matching in 3d with mast3r. In ECCV, 2024. 4, 6

    2024

  10. [17]

    A sequential algorithm for training text clas- sifiers: Corrigendum and additional data

    David D Lewis. A sequential algorithm for training text clas- sifiers: Corrigendum and additional data. In Acm Sigir Fo- rum. ACM New York, NY , USA, 1995. 2

    1995

  11. [18]

    Instant3D: Fast text-to-3D with sparse-view generation and large reconstruction model

    Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3D: Fast text-to-3D with sparse-view generation and large reconstruction model. In ICLR, 2024. 1

    2024

  12. [19]

    Learning the 3d fauna of the web

    Zizhang Li, Dor Litvak, Ruining Li, Yunzhi Zhang, Tomas Jakab, Christian Rupprecht, Shangzhe Wu, Andrea Vedaldi, and Jiajun Wu. Learning the 3d fauna of the web. In CVPR,

  13. [20]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019. 5

    2019

  14. [21]

    Srinivasan, Matthew Tancik, Jonathan T

    Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. In ECCV, 2020. 1, 2, 4

    2020

  15. [22]

    Blind/referenceless image spatial quality evaluator

    Anish Mittal, Anush K Moorthy, and Alan C Bovik. Blind/referenceless image spatial quality evaluator. In ASILOMAR. IEEE, 2011. 3, 5

    2011

  16. [23]

    completely blind

    Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Mak- ing a “completely blind” image quality analyzer. IEEE Sig- nal processing letters, 2012. 3, 5

    2012

  17. [24]

    Blind image quality assessment: From natural scene statistics to perceptual quality

    Anush Krishna Moorthy and Alan Conrad Bovik. Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE transactions on Image Processing,

  18. [25]

    Instant neural graphics primitives with a multires- olution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a multires- olution hash encoding. ACM TOG, 2022. 1, 2

    2022

  19. [27]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timoth ´ee Darcet, Th ´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 4, 8

    2023 arXiv

  20. [28]

    Ac- tivenerf: Learning where to see with uncertainty estimation

    Xuran Pan, Zihang Lai, Shiji Song, and Gao Huang. Ac- tivenerf: Learning where to see with uncertainty estimation. In ECCV, 2022. 1, 2, 3, 4, 5

    2022

  21. [29]

    Habitat 3.0: A co-habitat for humans, avatars and robots

    Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dal- laire Cote, Tsung-Yen Yang, Ruslan Partsey, Ruta Desai, Alexander William Clegg, Michal Hlavac, So Yeon Min, et al. Habitat 3.0: A co-habitat for humans, avatars and robots. arXiv preprint arXiv:2310.13724, 2023. 7 9

    arXiv

  22. [30]

    Neurar: Neural uncertainty for autonomous 3d reconstruction with implicit neural representations

    Yunlong Ran, Jing Zeng, Shibo He, Jiming Chen, Lincheng Li, Yingfeng Chen, Gimhee Lee, and Qi Ye. Neurar: Neural uncertainty for autonomous 3d reconstruction with implicit neural representations. IEEE Robotics and Automation Let- ters, 2023. 2

    2023

  23. [31]

    A survey of deep active learning

    Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B Gupta, Xiaojiang Chen, and Xin Wang. A survey of deep active learning. ACM computing surveys (CSUR), 2021. 2

    2021

  24. [32]

    Habitat: A Platform for Embodied AI Research

    Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra. Habitat: A Platform for Embodied AI Research. In ICCV, 2019. 7

    2019

  25. [33]

    Active learning literature survey

    Burr Settles. Active learning literature survey. Computer sciences technical report, University of Wisconsin–Madison,

  26. [34]

    Multiple- instance active learning

    Burr Settles, Mark Craven, and Soumya Ray. Multiple- instance active learning. In NeurIPS, 2007. 2

    2007

  27. [35]

    Query by committee

    H Sebastian Seung, Manfred Opper, and Haim Sompolin- sky. Query by committee. In Proceedings of the fifth annual workshop on Computational learning theory, 1992. 2

    1992

  28. [36]

    Habitat 2.0: Training home assistants to rearrange their habitat

    Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir V ondrus, Sameer Dharur, Franziska Meier, Wo- jciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun,...

    2021

  29. [37]

    Splatter image: Ultra-fast single-view 3d recon- struction

    Stanislaw Szymanowicz, Chrisitian Rupprecht, and Andrea Vedaldi. Splatter image: Ultra-fast single-view 3d recon- struction. In CVPR, 2024. 1

    2024

  30. [38]

    Nima: Neural image assessment

    Hossein Talebi and Peyman Milanfar. Nima: Neural image assessment. IEEE Transactions on Image Processing, 2018. 5

    2018

  31. [39]

    Lgm: Large multi-view gaus- sian model for high-resolution 3d content creation

    Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaus- sian model for high-resolution 3d content creation. InECCV,

  32. [40]

    Blind image quality evaluation using perception based features

    Narasimhan Venkatanath, D Praneeth, Maruthi Chan- drasekhar Bh, Sumohana S Channappayya, and Swarup S Medasani. Blind image quality evaluation using perception based features. In NCC. IEEE, 2015. 3, 5

    2015

  33. [41]

    Repvit: Revisiting mobile cnn from vit perspective

    Ao Wang, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding. Repvit: Revisiting mobile cnn from vit perspective. In CVPR, 2024. 4, 5, 8

    2024

  34. [42]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Process- ing, 2004. 3

    2004

  35. [43]

    Crossscore: Towards multi-view image evaluation and scor- ing

    Zirui Wang, Wenjing Bian, and Victor Adrian Prisacariu. Crossscore: Towards multi-view image evaluation and scor- ing. In ECCV, 2024. 2, 3, 4, 5

    2024

  36. [44]

    Magicpony: Learning articu- lated 3d animals in the wild

    Shangzhe Wu, Ruining Li, Tomas Jakab, Christian Rup- precht, and Andrea Vedaldi. Magicpony: Learning articu- lated 3d animals in the wild. In CVPR, 2023. 1

    2023

  37. [45]

    Zamir, Zhi-Yang He, Alexander Sax, Ji- tendra Malik, and Silvio Savarese

    Fei Xia, Amir R. Zamir, Zhi-Yang He, Alexander Sax, Ji- tendra Malik, and Silvio Savarese. Gibson Env: real-world perception for embodied agents. In CVPR, 2018. 7

    2018

  38. [46]

    A frontier-based approach for autonomous exploration

    Brian Yamauchi. A frontier-based approach for autonomous exploration. In IEEE International Symposium on Computa- tional Intelligence in Robotics and Automation, 1997. 8

    1997

  39. [47]

    Maniqa: Multi-dimension attention network for no-reference image quality assessment

    Sidi Yang, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujiu Yang. Maniqa: Multi-dimension attention network for no-reference image quality assessment. In CVPR, 2022. 5, 8

    2022

  40. [48]

    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. In CVPR, 2021. 1

    2021

  41. [49]

    Stereo magnification: Learning view syn- thesis using multiplane images

    Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view syn- thesis using multiplane images. ACM TOG, 2018. 5, 6, 7, 11 10 Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Ass...

    2018

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.