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REVIEW 4 major objections 7 minor 2 cited by

SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction

T0 review · 4 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read SPARS3R fuses a dense depth-prior point cloud with SfM-accurate poses through global Procrustes alignment and semantic local fixes, producing a Gaussian-splatting initialization that outperforms prior sparse-view NVS methods by about 2.7…

desk verdict Solid sparse-view NVS system: the semantic outlier alignment is genuinely new, but most of the reported gain comes from global fusion with SfM poses, and 'consistent' needs per-scene support. read the letter →

arxiv 2411.12592 v1 pith:AFAJX4IS submitted 2024-11-15 cs.CV

Yutao Tang , Yuxiang Guo , Deming Li , Cheng Peng This is my paper · ORCID
classification cs.CV
keywords Sparse-viewnovelviewsynthesis3DGaussianSplattingStructure-from-MotiondensepointcloudalignmentsemanticsegmentationMASt3Rdepthpriorcameraposeaccuracy
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

SPARS3R is a pipeline for sparse-view novel view synthesis. It argues that accurate-but-sparse Structure-from-Motion point clouds and dense-but-depth-inaccurate point clouds from learned two-view estimators should be combined rather than chosen between. The central claim is that a global rigid alignment of the dense cloud onto the SfM cloud, followed by piecewise rigid alignments inside semantic regions around residual outliers, yields a point cloud that is both dense and pose-consistent, and that initializing Gaussian splatting with this cloud improves rendering quality under sparse views. On three benchmarks the method reports consistent gains over previous approaches, such as PSNR 18.85 versus 16.23 on MipNeRF360. The result matters because it indicates that semantic segmentation can repair geometric biases in learned depth without sacrificing the pose accuracy of classical SfM.

What carries the argument

The central mechanism is global-plus-semantic alignment. Global Fusion Alignment uses RANSAC-robust Procrustes analysis on forward-backward projected correspondences to map the dense prior onto the sparse SfM cloud, producing a single scale, rotation, and translation. Semantic Outlier Alignment then iteratively seeds masks from the Segment-Anything Model at outlier projections, requiring each mask to contain at least a threshold number $T$ of SfM outliers before estimating a per-mask rigid transform. The output is a fused point cloud $\chi^*$ concatenated with the SfM point cloud, which serves as the initialization for a Gaussian splatting optimizer. The intended effect is that accurate SfM poses and dense depth coverage coexist, so optimization avoids floaters and background blur.

What would settle it

On a scene with a large smooth curved surface, measure the residual alignment error inside a single semantic mask after Semantic Outlier Alignment; if the error grows with depth bias or the mask lacks SfM support, the piecewise-rigid assumption fails.

Watch

Extended reading notes

Core claim

The discovery is a two-stage alignment recipe. Given a dense point cloud from MASt3R/DUSt3R and a sparse point cloud from COLMAP SfM, correspondences are obtained by projecting SfM points into the dense cloud's coordinate frame, and a RANSAC-filtered Procrustes fit provides a global transform. Points that remain outliers are grouped by prompting the Segment-Anything Model with their projected 2D locations; each semantic mask is then given its own local rigid transform estimated from the SfM points inside it. The transformed dense cloud is concatenated with the SfM cloud and used to initialize Gaussian splatting. The paper reports that this fused initialization consistently outperforms existing sparse-view NVS methods across MipNeRF360, Tanks & Temples, and MVimgNet, with an average PSNR gain of about 2.7 dB.

Load-bearing premise

The load-bearing premise is that the depth errors left after a global rigid fit are rigid per semantically coherent region, and every such region has enough SfM points to estimate its own local transform.

Editorial extensions

If this is right

  • Sparse-view Gaussian splatting can be initialized with both dense coverage and SfM-accurate poses instead of trading one against the other.
  • Residual depth bias in learned dense clouds can be corrected locally by grouping outlier regions with semantic masks, as long as each group has enough SfM support.
  • Sparse-NVS evaluation becomes fairer when test camera poses are aligned with rotation-aware RANSAC and quality is measured with a pose-shift-robust metric such as DSIM.
  • On the three tested benchmarks, the method reports the best sparse-view NVS numbers, with the largest gain over the strongest prior baseline on MipNeRF360.

Reading between the lines

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

  • Editorial inference: The same global-plus-semantic alignment recipe should transfer to other dense depth estimators beyond MASt3R/DUSt3R, provided their depth errors concentrate at object boundaries; swapping the prior and rerunning the fusion would test this.
  • Editorial inference: The evaluation changes imply that earlier sparse-NVS numbers relying on PSNR/SSIM under imperfect test-pose alignment may undervalue methods with accurate geometry; DSIM plus rotation-aware camera alignment could become a fairer default.
  • Editorial inference: A natural extension, named by the paper as future work, is replacing the rigid-per-mask transform with a smooth non-rigid deformation inside each semantic region; an intermediate test is to fit locally affine rather than rigid transforms per mask.
  • Editorial inference: Because the fused cloud inherits SfM's scale and pose accuracy, it could serve as a metric-scale geometric prior for robotics or navigation, not only as a rendering initialization.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 7 minor

Summary. SPARS3R is a sparse-view 3D reconstruction and novel view synthesis method. It combines a sparse point cloud and camera poses from COLMAP SfM (run with MASt3R feature matches) with a dense point cloud from MASt3R/DUSt3R. In the first stage, Global Fusion Alignment estimates a global similarity transform (Procrustes with RANSAC) that maps the dense prior point cloud into the SfM coordinate frame. In the second stage, Semantic Outlier Alignment groups the remaining alignment outliers by prompting SAM with outlier points, and estimates a local rigid transform per semantic mask; the locally transformed points are merged with the SfM cloud to initialize a 3DGS/Splatfacto optimization. The paper also contributes evaluation modifications: a rotation-point-aware Procrustes alignment with RANSAC for camera pose evaluation, and the use of DreamSim (DSIM) as an additional perceptual metric. Experiments on MipNeRF360, Tanks & Temples, and MVimgNet report improved PSNR, SSIM, LPIPS, and DSIM over existing sparse-view NVS methods, with the largest gains on MipNeRF360 (e.g., 18.85 vs. 16.23 PSNR over InstantSplat).

Significance. If the results hold, SPARS3R is a practical and conceptually simple way to combine the dense geometric prior of DUSt3R/MASt3R with the accurate camera poses of SfM, and the reported margins over InstantSplat and FSGS on three benchmarks are substantial. The paper ships code, builds on publicly available components, and includes a useful evaluation improvement (rotation-point camera alignment with RANSAC). However, the novel Semantic Outlier Alignment step is currently supported by only a 0.3 dB average PSNR gain in a one-dataset ablation, and the absence of per-scene statistics and error bars weakens the claim that SPARS3R 'consistently' outperforms prior methods. The test-pose-optimization protocol and the non-standard DSIM metric also require clearer justification before the quantitative claims can be fully assessed.

major comments (4)
  1. [4.2 / Table 2] The only ablation of Semantic Outlier Alignment is the MipNeRF360 average, where the full SPARS3R pipeline reaches 18.9 dB PSNR versus 18.6 dB for Global Fusion Alignment alone. The text mentions a 1.4 dB gain on the Bonsai scene, but no per-scene breakdown or variance estimate is given for the nine MipNeRF360 scenes or for the other two datasets. Because Section 4.3 claims that SPARS3R 'consistently improves' over prior methods, and Table 4 reports only dataset averages over 24 scenes, the evidence does not yet rule out the possibility that SOA helps one or two scenes and slightly hurts others. Please provide per-scene results, standard deviations or error bars, and ablations on Tanks & Temples and MVimgNet.
  2. [3.2.2 / Algorithm 1] The RANSAC parameters (sample size n, number of iterations R, error threshold epsilon) and the mask support threshold T in Eq. (5) are never specified. These parameters directly determine the inlier/outlier partition and which semantic masks are accepted for local alignment, so they have a direct effect on the fused point cloud and on the reported render metrics. Without these values (or a sensitivity analysis), the experiments are not reproducible, and the 0.3 dB SOA gain cannot be assessed for threshold dependence.
  3. [3.2.2 / Section 4.4] The piecewise-rigid assumption per SAM mask is the core of Semantic Outlier Alignment, but the manuscript's own limitations state that masks can split connected surfaces (leaving insufficient SfM support) or group areas with disparate depths (making local alignment ineffective). The paper gives no indication of how often these failure modes occur across the 24 scenes, no sensitivity to SAM mask granularity, and no diagnostic of the fraction of outliers that are discarded rather than aligned. Please quantify these cases and their effect on the final render metrics.
  4. [4.1 / Table 4] All methods are evaluated after 500 steps of test-pose optimization using the test images. This post-processing fits renders to the ground-truth test views and can differentially inflate PSNR/SSIM across methods, so the headline numbers in Table 4 may reflect pose-fitting ability rather than pure NVS quality. Please report metrics with and without test-pose optimization, and specify exactly what is optimized and whether ground-truth test images are used in that optimization.
minor comments (7)
  1. [Section 1, Contribution list] The word 'Gloabl' should be 'Global'.
  2. [Section 3.2.1] The word 'btween' should be 'between'.
  3. [Figure 1 caption] The method name is misspelled as 'SPAS3R'; it should be 'SPARS3R'.
  4. [Section 3.2.1] The notation for the visibility indicator, 'V in R^{N*3} -> R^{N*3}', is unclear; please define V as a function or set-valued map.
  5. [Section 4.1 / Figure 3] The claim that DSIM is the most pose-shift invariant metric is based only on the qualitative curves in Figure 3; please provide numeric values or a table.
  6. [Table 3] SPARS3R itself is not listed in Table 3; since SPARS3R uses COLMAP poses, please state explicitly that its pose accuracy is that of the COLMAP+MASt3R row.
  7. [Table 4 header] The dataset name is misspelled as 'MipsNeRF360'; it should be 'MipNeRF360'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SPARS3R's components are independently fitted to reconstruction inputs and evaluated on held-out views.

full rationale

SPARS3R's derivation chain is self-contained rather than circular. The dense point cloud comes from the external pretrained MASt3R/DUSt3R models, the sparse reference point cloud and poses come from COLMAP SfM, and the semantic grouping comes from the external SAM model. The global and local rigid alignments in Eqs. (4), (6), and (7) are fitted to SfM correspondences, not to the reported novel-view-synthesis metrics. The rendering quality is then evaluated on held-out test views after Gaussian optimization, so no fitted parameter is renamed as a prediction. The paper's introduction of DSIM as an evaluation metric is not circular: DreamSim is an external pretrained similarity model, the method's parameters are not optimized against DSIM, and PSNR, SSIM, and LPIPS also improve in Table 4. Section 4.4 honestly states limitations of the semantic-mask and piecewise-rigid assumptions; those are robustness caveats, not reductions of the method's output to its inputs. No load-bearing self-citations, imported uniqueness theorems, or ansatz-by-citation steps are present. Therefore the circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method is an engineering pipeline built on pretrained models (DUSt3R, MASt3R, SAM) and standard SfM. The load-bearing assumptions are about the spatial structure of depth bias and the reliability of SfM anchors. Hyperparameters for RANSAC and mask selection are not disclosed in the main text, which is a reproducibility gap.

free parameters (4)
  • RANSAC inlier threshold epsilon
    Not specified numerically; described as empirical in Section 3.2.1. Controls the split between inliers and outliers in Global Fusion Alignment, hence the set of points sent to Semantic Outlier Alignment.
  • Outlier mask threshold T
    Minimum number of outlier SfM correspondences required in a SAM mask to keep it (Eq. 5). Not specified. Determines which masks are retained for local alignment.
  • RANSAC sample size n and iterations R
    Parameters of Algorithm 1, not specified.
  • Loss weights lambda1 and lambda2 = 0.8 and 0.2
    Weights in Eq. 9, specified in Implementation Details (Section 4.1).
assumptions (4)
  • domain assumption The dense point cloud from DUSt3R/MASt3R is locally coherent but globally biased toward smoothness between objects.
    Motivates the two-stage alignment; stated in Section 3.2.1.
  • domain assumption The SfM point cloud from COLMAP with MASt3R features has accurate poses and reliable geometry under sparse views.
    Used as anchor in Eq. 4 and throughout; based on prior SfM literature, not re-validated here.
  • ad hoc to paper Scene regions corresponding to SAM masks each follow a single rigid transformation.
    Core of Semantic Outlier Alignment, Eq. 6. Paper's own limitations (Section 4.4) note non-rigid transforms are not handled.
  • domain assumption SAM segmentation groups outliers into semantically coherent regions with consistent depth bias.
    Assumed in the iterative mask selection (Eq. 5).

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Cite this review

Pith. "Pith review of SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction." pith.science (2026). https://pith.science/paper/AFAJX4IS

@misc{pith2026241112592,
  author       = {Pith},
  title        = {Pith review of: SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AFAJX4IS}},
  note         = {Machine review of arXiv:2411.12592}
}
read the original abstract

Recent efforts in Gaussian-Splat-based Novel View Synthesis can achieve photorealistic rendering; however, such capability is limited in sparse-view scenarios due to sparse initialization and over-fitting floaters. Recent progress in depth estimation and alignment can provide dense point cloud with few views; however, the resulting pose accuracy is suboptimal. In this work, we present SPARS3R, which combines the advantages of accurate pose estimation from Structure-from-Motion and dense point cloud from depth estimation. To this end, SPARS3R first performs a Global Fusion Alignment process that maps a prior dense point cloud to a sparse point cloud from Structure-from-Motion based on triangulated correspondences. RANSAC is applied during this process to distinguish inliers and outliers. SPARS3R then performs a second, Semantic Outlier Alignment step, which extracts semantically coherent regions around the outliers and performs local alignment in these regions. Along with several improvements in the evaluation process, we demonstrate that SPARS3R can achieve photorealistic rendering with sparse images and significantly outperforms existing approaches.

Figures

Figures reproduced from arXiv: 2411.12592 by the authors.

Figure 1
Figure 1. A visualization of SPAS3R in comparison to cur [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. SPARS3R combines a prior dense point cloud [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Evaluation of different metrics to camera pose shift. We [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The trajectory of the camera poses estimated from dif [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Visual comparison of SPARS3R with and without SOA. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Visual comparisons of different NVS methods on MipNeRF360 [ [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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    A pose-free deblurring 3D Gaussian Splatting pipeline using DUSt3R point clouds, confidence-balanced sampling, and event-decoded latent image supervision.

Reference graph

Works this paper leans on

61 extracted references · 23 canonical work pages · cited by 2 Pith papers

  1. [1]

    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 Proceedings of the IEEE/CVF inter- national conference on computer vision , pages 5855–5864,

  2. [2]

    Mip-nerf 360: Unbounded anti-aliased neural radiance fields

    Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5470–5479, 2022. 5, 6, 7, 8

    work page 2022

  3. [3]

    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. 2

    2023

  4. [4]

    Hexplane: A fast representa- tion for dynamic scenes

    Ang Cao and Justin Johnson. Hexplane: A fast representa- tion for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 130–141, 2023. 2

    2023

  5. [5]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. InEuropean con- ference on computer vision, pages 333–350. Springer, 2022. 2

    2022

  6. [6]

    Nerf-hugs: Improved neural radiance fields in non-static scenes using heuristics-guided segmentation

    Jiahao Chen, Yipeng Qin, Lingjie Liu, Jiangbo Lu, and Guanbin Li. Nerf-hugs: Improved neural radiance fields in non-static scenes using heuristics-guided segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 19436–19446, 2024. 2

    2024

  7. [7]

    Aug-nerf: Training stronger neural radiance fields with triple-level physically-grounded augmentations

    Tianlong Chen, Peihao Wang, Zhiwen Fan, and Zhangyang Wang. Aug-nerf: Training stronger neural radiance fields with triple-level physically-grounded augmentations. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15191–15202, 2022. 2

    work page 2022

  8. [8]

    Hallucinated neural radiance fields in the wild

    Xingyu Chen, Qi Zhang, Xiaoyu Li, Yue Chen, Ying Feng, Xuan Wang, and Jue Wang. Hallucinated neural radiance fields in the wild. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 12943–12952, 2022. 2

    2022

Show all 61 references
  1. [9]

    Gaussianpro: 3d gaussian splatting with progressive propagation

    Kai Cheng, Xiaoxiao Long, Kaizhi Yang, Yao Yao, Wei Yin, Yuexin Ma, Wenping Wang, and Xuejin Chen. Gaussianpro: 3d gaussian splatting with progressive propagation. InForty- first International Conference on Machine Learning, 2024. 2

    2024

  2. [10]

    Depth-regularized optimization for 3d gaussian splatting in few-shot images

    Jaeyoung Chung, Jeongtaek Oh, and Kyoung Mu Lee. Depth-regularized optimization for 3d gaussian splatting in few-shot images. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 811–820, 2024. 7

    2024

  3. [11]

    Depth-regularized optimization for 3d gaussian splatting in few-shot images

    Jaeyoung Chung, Jeongtaek Oh, and Kyoung Mu Lee. Depth-regularized optimization for 3d gaussian splatting in few-shot images. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 811–820, 2024. 2, 7

    2024

  4. [12]

    Depth-supervised nerf: Fewer views and faster train- ing for free

    Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ra- manan. Depth-supervised nerf: Fewer views and faster train- ing for free. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12882– 12891, 2022. 1, 5

    2022

  5. [13]

    Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps

    Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, De- jia Xu, and Zhangyang Wang. Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps. arXiv preprint arXiv:2311.17245, 2023. 2

    2023 arXiv

  6. [14]

    Instantsplat: Un- bounded sparse-view pose-free gaussian splatting in 40 sec- onds

    Zhiwen Fan, Wenyan Cong, Kairun Wen, Kevin Wang, Jian Zhang, Xinghao Ding, Danfei Xu, Boris Ivanovic, Marco Pavone, Georgios Pavlakos, et al. Instantsplat: Un- bounded sparse-view pose-free gaussian splatting in 40 sec- onds. arXiv preprint arXiv:2403.20309, 2, 2024. 1, 2, 5, 6, 7

    2024 arXiv

  7. [15]

    Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981

    Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM, 24(6):381–395, 1981. 2, 5

    1981

  8. [16]

    Plenoxels: Radiance fields without neural networks

    Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5501–5510, 2022. 2

    2022

  9. [17]

    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. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 12479–12488, 2023. 2

    2023

  10. [18]

    Dream- sim: Learning new dimensions of human visual similarity using synthetic data, 2023

    Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dream- sim: Learning new dimensions of human visual similarity using synthetic data, 2023. 6

    2023

  11. [19]

    Efros, and Xiaolong Wang

    Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, and Xiaolong Wang. Colmap-free 3d gaussian splat- ting. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 20796– 20805, 2024. 7

    2024

  12. [20]

    Fastnerf: High-fidelity neu- ral rendering at 200fps

    Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. Fastnerf: High-fidelity neu- ral rendering at 200fps. In Proceedings of the IEEE/CVF international conference on computer vision , pages 14346– 14355, 2021. 2

    2021

  13. [21]

    Ea- gles: Efficient accelerated 3d gaussians with lightweight en- codings

    Sharath Girish, Kamal Gupta, and Abhinav Shrivastava. Ea- gles: Efficient accelerated 3d gaussians with lightweight en- codings. arXiv preprint arXiv:2312.04564, 2023. 2

    2023 arXiv

  14. [22]

    Generalized procrustes analysis

    John C Gower. Generalized procrustes analysis. Psychome- trika, 40:33–51, 1975. 4, 5

    1975

  15. [23]

    Putting nerf on a diet: Semantically consistent few-shot view synthesis

    Ajay Jain, Matthew Tancik, and Pieter Abbeel. Putting nerf on a diet: Semantically consistent few-shot view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5885–5894, 2021. 1, 5

    2021

  16. [24]

    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 Trans. Graph., 42(4):139–1,

  17. [25]

    Infonerf: Ray entropy minimization for few-shot neural volume ren- dering

    Mijeong Kim, Seonguk Seo, and Bohyung Han. Infonerf: Ray entropy minimization for few-shot neural volume ren- dering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 12912– 12921, 2022. 1, 5

    2022

  18. [26]

    Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C. Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick. Segment anything. arXiv:2304.02643, 2023. 2, 6

    2023 arXiv

  19. [27]

    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 Transactions on Graphics (ToG) , 36 (4):1–13, 2017. 5, 7

    2017

  20. [28]

    Compact 3d gaussian representation for radiance field

    Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, and Eunbyung Park. Compact 3d gaussian representation for radiance field. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21719– 21728, 2024. 2

    2024

  21. [29]

    Ground- ing image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and J´erˆome Revaud. Ground- ing image matching in 3d with mast3r. arXiv preprint arXiv:2406.09756, 2024. 2, 3, 4, 5, 6

    2024 arXiv

  22. [31]

    Dngaussian: Optimizing sparse-view 3d gaussian radiance fields with global-local depth normaliza- tion

    Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xin Ning, Jun Zhou, and Lin Gu. Dngaussian: Optimizing sparse-view 3d gaussian radiance fields with global-local depth normaliza- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 2...

  23. [32]

    Barf: Bundle-adjusting neural radiance fields

    Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Si- mon Lucey. Barf: Bundle-adjusting neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5741–5751, 2021. 6

    2021

  24. [33]

    Scaffold-gs: Structured 3d gaussians for view-adaptive rendering

    Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20654–20664, 2024. 2

    2024

  25. [34]

    Nerf in the wild: Neural radiance fields for uncon- strained photo collections

    Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duck- worth. Nerf in the wild: Neural radiance fields for uncon- strained photo collections. In Proceedings of the IEEE/CVF conference on computer vision and pattern recogn...

    2021

  26. [35]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    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. Communications of the ACM, 65(1):99–106, 2021. 1, 2, 7

    2021

  27. [36]

    Instant neural graphics primitives with a mul- tiresolution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a mul- tiresolution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022. 1, 2, 7

    2022

  28. [37]

    Compgs: Smaller and faster gaussian splatting with vector quantization

    KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash. Compgs: Smaller and faster gaussian splatting with vector quantization. In Euro- pean Conference on Computer Vision, 2024. 2

    2024

  29. [38]

    Compressed 3d gaussian splatting for accelerated novel view synthesis

    Simon Niedermayr, Josef Stumpfegger, and R ¨udiger West- ermann. Compressed 3d gaussian splatting for accelerated novel view synthesis. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 10349–10358, 2024. 2

    2024

  30. [39]

    Reg- nerf: 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. Reg- nerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition,...

    2022

  31. [40]

    Radsplat: Radiance field-informed gaussian splat- ting for robust real-time rendering with 900+ fps

    Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakoto- saona, Michael Oechsle, Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, and Federico Tombari. Radsplat: Radiance field-informed gaussian splat- ting for robust real-time rendering with 900+ fps. ...

    2024 arXiv

  32. [41]

    Coherentgs: Sparse novel view synthesis with coherent 3d gaussians

    Avinash Paliwal, Wei Ye, Jinhui Xiong, Dmytro Kotovenko, Rakesh Ranjan, Vikas Chandra, and Nima Khademi Kalan- tari. Coherentgs: Sparse novel view synthesis with coherent 3d gaussians. In European Conference on Computer Vision, pages 19–37. Springer, 2025. 2

    2025

  33. [42]

    Nerfies: Deformable neural radiance fields

    Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865–5874, 2021. 2

    2021

  34. [43]

    D-nerf: Neural radiance fields for dynamic scenes

    Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 10318–10327, 2021. 2

    2021

  35. [44]

    Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps

    Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE/CVF international conference on computer vision , pages 14335– 14345, 2021. 2

    2021

  36. [45]

    Structure-from-motion revisited

    Johannes Lutz Sch ¨onberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Com- puter Vision and Pattern Recognition (CVPR) , 2016. 2, 5, 6

    2016

  37. [46]

    Tensor4d: Efficient neural 4d decomposition for high-fidelity dynamic reconstruction and rendering

    Ruizhi Shao, Zerong Zheng, Hanzhang Tu, Boning Liu, Hongwen Zhang, and Yebin Liu. Tensor4d: Efficient neural 4d decomposition for high-fidelity dynamic reconstruction and rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 166...

    2023

  38. [47]

    Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction

    Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 5459– 5469, 2022. 2

    2022

  39. [48]

    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. In ACM SIGGRAPH 2023 Conference Proceedings , pages 1–12...

    2023

  40. [49]

    Ref-nerf: Struc- tured view-dependent appearance for neural radiance fields

    Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T Barron, and Pratul P Srinivasan. Ref-nerf: Struc- tured view-dependent appearance for neural radiance fields. In 2022 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 5481–5490. IE...

    2022

  41. [50]

    Sparsenerf: Distilling depth ranking for few-shot novel view synthesis

    Guangcong Wang, Zhaoxi Chen, Chen Change Loy, and Zi- wei Liu. Sparsenerf: Distilling depth ranking for few-shot novel view synthesis. InProceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 9065–9076,

  42. [51]

    Dust3r: Geometric 3d vi- sion made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vi- sion made easy. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20697– 20709, 2024. 1, 2, 3, 4, 6, 7

    2024

  43. [52]

    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 processing, 13(4):600–612, 2004. 5

    2004

  44. [53]

    Dˆ 2nerf: Self-supervised decoupling of dynamic and static objects from a monocular video

    Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, For- rester Cole, and Cengiz Oztireli. Dˆ 2nerf: Self-supervised decoupling of dynamic and static objects from a monocular video. Advances in neural information processing systems , 35:32653–32666, 2022. 2

    2022

  45. [54]

    Sparsegs: Real- time 360 tzdegu sparse view synthesis using gaussian splat- ting

    Haolin Xiong, Sairisheek Muttukuru, Rishi Upadhyay, Pradyumna Chari, and Achuta Kadambi. Sparsegs: Real- time 360 tzdegu sparse view synthesis using gaussian splat- ting. arXiv preprint arXiv:2312.00206, 2023. 1, 2, 7

    2023 arXiv

  46. [55]

    Multi-scale 3d gaussian splatting for anti-aliased rendering

    Zhiwen Yan, Weng Fei Low, Yu Chen, and Gim Hee Lee. Multi-scale 3d gaussian splatting for anti-aliased rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20923–20931, 2024. 2

    2024

  47. [56]

    Freenerf: Im- proving few-shot neural rendering with free frequency reg- ularization

    Jiawei Yang, Marco Pavone, and Yue Wang. Freenerf: Im- proving few-shot neural rendering with free frequency reg- ularization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8254–8263,

  48. [57]

    Cross-ray neural radiance fields for novel- view synthesis from unconstrained image collections

    Yifan Yang, Shuhai Zhang, Zixiong Huang, Yubing Zhang, and Mingkui Tan. Cross-ray neural radiance fields for novel- view synthesis from unconstrained image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15901–15911, 2023. 2

    2023

  49. [58]

    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 Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 4578–4587, 2021. 5

    2021

  50. [59]

    Mvimgnet: A large-scale dataset of multi-view images

    Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, et al. Mvimgnet: A large-scale dataset of multi-view images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognit...

    2023

  51. [60]

    Mip-splatting: Alias-free 3d gaussian splat- ting

    Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splat- ting. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 19447–19456,

  52. [61]

    Pixel-gs: Density control with pixel- aware gradient for 3d gaussian splatting

    Zheng Zhang, Wenbo Hu, Yixing Lao, Tong He, and Hengshuang Zhao. Pixel-gs: Density control with pixel- aware gradient for 3d gaussian splatting. arXiv preprint arXiv:2403.15530, 2024. 2

    2024 arXiv

  53. [62]

    Fsgs: Real-time few-shot view synthesis using gaussian splatting

    Zehao Zhu, Zhiwen Fan, Yifan Jiang, and Zhangyang Wang. Fsgs: Real-time few-shot view synthesis using gaussian splatting. In European Conference on Computer Vision , pages 145–163. Springer, 2025. 1, 2, 5, 7

    2025

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.