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REVIEW 4 major objections 6 minor 4 cited by

GLS: Geometry-aware 3D Language Gaussian Splatting

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read One 3D Gaussian model can reconstruct indoor surfaces and segment open-vocabulary objects at the same time, and the paper shows the two tasks improve each other.

desk verdict The joint reconstruction + segmentation idea is genuinely useful, but the headline numbers don't add up: Tables 1, 4, and 7 disagree with each other, so the SOTA claim needs the authors' own tables reconciled before I'd trust it. read the letter →

arxiv 2411.18066 v2 pith:G4J3ZILC submitted 2024-11-27 cs.CV

Jiaxiong Qiu , Liu Liu , Xinjie Wang , Tianwei Lin , Wei Sui , Zhizhong Su This is my paper · ORCID
classification cs.CV
keywords 3DGaussianSplattingindoorsurfacereconstructionopen-vocabularysegmentationnormalpriorCLIPfeaturesDEVAmasksdepthrefinementTSDFfusion
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 proposes GLS, a single 3D Gaussian Splatting framework that performs indoor surface reconstruction and 3D open-vocabulary segmentation together, rather than as separate tasks. Its central claim is that the two tasks share the same goal—sharp, smooth surfaces—so optimizing them jointly improves both: geometric normal priors sharpen object boundaries for segmentation, while semantic CLIP and DEVA cues smooth texture-less and reflective surfaces for reconstruction. The method introduces three regularization terms: a normal-prior loss $L_n$, a semantic-guided normal smoothing loss $L_s$, and a normal-error-guided depth refinement loss $L_d$. On MuSHRoom, ScanNet++, and LERF-OVS, GLS reports better accuracy than single-task state-of-the-art methods on both tasks, with training time comparable to vanilla 3DGS.

What carries the argument

The load-bearing mechanism is the joint optimization objective over Gaussian primitives that carry both geometry and semantic features. Three regularization terms do the work: $L_n$ takes a monocular normal prior and pulls the rendered surface normal toward it, weighted by opacity; $L_s$ uses CLIP feature gradients to decide where to smooth normals, restricted to the largest surfaces via SAM masks; $L_d$ partitions each pixel into one of three angular cases relating the rendered normal to the camera's $-y$ axis, then replaces the rendered depth with a piecewise target depth built from the rendered depth, the unbiased depth, and the rendered alpha, and penalizes the difference with an exponential loss. The semantic branch supervises per-Gaussian features with CLIP features and DEVA-derived view-consistent masks, and meshes are extracted by TSDF fusion of the refined depth.

What would settle it

On a synthetic indoor scene with ground-truth normals and depth, disable every loss except $L_d$ and measure whether optimized depth moves toward the ground truth for pixels whose true normal lies near or outside the three angular sectors; if those pixels systematically degrade, the three-case partition is not exhaustive.

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Extended reading notes

Core claim

The authors aim to establish that surface reconstruction and open-vocabulary segmentation are complementary enough that a single Gaussian representation can be optimized for both at once, and that doing so yields sharper and smoother results than either task alone. Concretely, they show that using a monocular normal prior to regularize the normal derived from rendered depth, using CLIP features to smooth normals only on large surfaces, and using the angular error between rendered and ideal normals to refine the unbiased depth, all improve both reconstruction metrics (accuracy, completion, chamfer-L1, normal consistency, F-score) and segmentation metrics (mIoU and boundary IoU). The same learned semantic features that answer text queries also act as a smoothness prior for geometry, and the cleaned geometry in turn gives segmentation cleaner boundaries.

Load-bearing premise

The depth-refinement loss $L_d$ assumes that the true surface normal always falls into one of three angular sectors defined around the rendered normal and the camera's $-y$ axis, so any real indoor normal outside those sectors would make the refined depth target wrong.

Editorial extensions

If this is right

  • A single trained 3DGS model can output a mesh and answer open-vocabulary text queries, so interactive scene understanding and geometry editing no longer require separate 3D reconstructions.
  • The geometry branch inherits semantic smoothness in texture-less regions, which is where monocular normal priors alone typically fail.
  • The semantic branch inherits sharper object boundaries from the geometry branch, as shown by gains in boundary IoU.
  • Because the losses are lightweight regularizers on top of 3DGS, the training cost stays near that of a single-task 3DGS run.
  • The sensitivity ablations show that each of the five losses contributes to both tasks, supporting the claim that joint optimization is what drives the improvement.

Reading between the lines

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

  • The three-case angular partition in $L_d$ is presented without a proof of exhaustiveness; if real indoor normals frequently fall outside or on the boundaries of these sectors, the depth-refinement loss could push geometry away from true surfaces, so an oracle-normal experiment would isolate this risk.
  • The same semantic-as-geometric-prior idea could transfer to other explicit representations, such as surfel or mesh-based pipelines, whenever texture-less or specular regions defeat photometric cues.
  • Since $L_s$ only regularizes the top-k largest objects, small-object scenes may not receive the smoothing benefit; a per-object adaptive weighting scheme is a natural extension.
  • A direct extension would be to make the normal-prior confidence view-dependent, because the paper notes monocular normal estimates become inconsistent in outdoor scenes; confidence weighting might broaden applicability.
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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 / 6 minor

Summary. The paper proposes GLS, a 3D Gaussian Splatting framework that jointly optimizes indoor surface reconstruction and 3D open-vocabulary segmentation. It introduces three regularization terms: a normal-prior loss Ln that guides rendered normals with a pretrained normal estimator, a semantic-feature-guided normal smoothing loss Ls that uses CLIP features to smooth large surfaces, and a normal-error-guided depth refinement loss Ld that recombines rendered depth, unbiased depth, and alpha masks according to a three-case partition of the ideal normal direction. The segmentation branch supervises Gaussian semantic features with DEVA masks and CLIP features. The paper claims state-of-the-art results on MuSHRoom, ScanNet++, and LERF-OVS, with ablations showing the contribution of each loss term.

Significance. The core idea of coupling geometric and semantic cues through shared 3D Gaussian primitives is timely and could be practically valuable if the reported gains hold. The paper evaluates on public datasets, reports training times, and includes qualitative demonstrations of interactive applications. The main contribution, however, is empirical, and the printed quantitative evidence is not internally consistent. Several tables that should describe the same configuration disagree, and one per-scene row appears to duplicate another dataset's row. These inconsistencies make the headline SOTA claim unverifiable as printed. The geometric derivation of Ld also lacks an exhaustiveness argument. With corrected and reconciled evaluations, the paper could make a useful contribution; as submitted, the evidence requires substantial clarification.

major comments (4)
  1. [§5.2 and §5.4, Tables 1 and 4] The full model without sensor depth is reported inconsistently: Table 1 lists Accuracy 0.0538, Normal Consistency 0.8357, and F-score 0.6922 for 'Ours ×', while Table 4's 'All' row, described as the full model without sensor depth, reports Accuracy 0.0814, Normal Consistency 0.8474, and F-score 0.5127. If these are the same setting, the discrepancy is unexplained and prevents verification of the central SOTA claim; if Table 4 uses a different split or protocol, the caption or text must state this explicitly.
  2. [§D, Table 7 vs Table 1] Averaging the five MuSHRoom per-scene rows with sensor depth from Table 7 gives Accuracy 0.0288, Completion 0.0269, Chamfer-L1 0.0310, Normal Consistency 0.8830, and F-score 0.8796, whereas Table 1 reports 0.0288, 0.0254, 0.0271, 0.8640, and 0.8924; only Accuracy agrees. The paper must reconcile these numbers or clearly state which table is the official evaluation result, since the discrepancy directly affects the claimed superiority over DN-Splatter and FDS.
  3. [§D, Table 7 'kokko' row and §5.2, Table 2] The 'kokko' row in Table 7 (Accuracy 0.0305, Completion 0.0272, Chamfer-L1 0.0444, Normal Consistency 0.9064, F-score 0.8623) is identical in four of five metrics to the ScanNet++ row in Table 2, with only Accuracy differing. This strongly suggests a transcription or copy-paste error and makes the per-scene evaluation unreliable as printed.
  4. [§4.3, Eq. (7) and Fig. 4] The three-case partition of the ideal normal n' is not shown to be exhaustive. The conditions M1: cos(α)>cos(θ')>0, M2: cos(α)<0, and M3: 0<cos(α)≤cos(θ') leave uncovered pixels with cos(θ')≤0, and the boundary cos(α)=0 is assigned to none of the masks. In addition, Fig. 4 presents the argument in a two-dimensional plane, while n' is a unit vector on the sphere; directions outside that plane are not accounted for. Since Ld is a core contribution, the paper needs either a rigorous proof of exhaustiveness or an experiment reporting the fraction of pixels falling into each mask and the behavior of the loss on unassigned pixels.
minor comments (6)
  1. [§5.1] The sentence 'We and the same tool of DN-Splatter...' is ungrammatical and should be rewritten, for example as 'We use the same evaluation tool as DN-Splatter...'.
  2. [§5.4] The word 'rexonstruction' in the last paragraph is a typo and should be 'reconstruction'.
  3. [§3, Eq. (3)] The rendered mask M used in the cross-entropy loss Lm is not defined alongside rendered color, depth, and features; it should be added to Eq. (3) or defined explicitly in the text.
  4. [§4.3] The threshold N_d^T \hat N < 0.9 for applying Ld is introduced without justification or ablation; a sensitivity analysis or a brief rationale would help.
  5. [Table 3] The LangSplat row reports only five mIoU values and no per-scene mBIoU values despite the header implying both sets of columns; reformat the table to make the missing entries explicit rather than leaving dashes.
  6. [Throughout] The name 'DEV A' should be written as 'DEVA', and the supplementary text contains the typo 'resconstructed' for 'reconstructed'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark claims are empirical and self-contained, with external priors and independent evaluations.

full rationale

GLS is an empirical method paper, not a derivation from first principles. The geometric and semantic priors come from external, pretrained models: DSINE for surface normals, OpenCLIP/LangSplat for CLIP features, and DEVA for view-consistent masks. None of these priors is fitted to the MuSHRoom, ScanNet++, or LERF-OVS benchmark targets, and the paper does not use benchmark numbers to set its loss weights. The central claim is that the full loss combination (Eq. 9) improves reconstruction and open-vocabulary segmentation metrics relative to rerun baselines, which is falsifiable by independent re-execution on the same splits. The ablations in Tables 4 and 5 compare the full model against variants with one term removed, so the contribution of each loss is demonstrated rather than assumed by construction. There is no load-bearing self-citation chain and no uniqueness theorem imported from the authors' own prior work; the cited prior methods (2DGS, PGSR, DN-Splatter, LangSplat, OpenGaussian, Gaussian Grouping) are independent external work. The depth-refinement loss Ld (Eqs. 7-8) does rely on an unproven three-sector partition of normal space, and Dr is a constructed blend of rendered and unbiased depth rather than an independently measured target. However, Ld is presented as a regularizer, not as a prediction derived from first principles, so this is an assumption about scene geometry and a potential robustness limitation, not a circular reduction. The internal inconsistencies across Tables 1, 2, 4, and 7 are a correctness and reproducibility concern about the printed SOTA claim, not a circularity concern. Under the stated criteria, no specific equation or fitted parameter is renamed as a prediction, and no derivation step reduces to its own input.

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

The method introduces no new physical entities. Its assumptions are concentrated on the reliability of external 2D priors and on the exhaustiveness of the geometric case analysis used to construct the depth-refinement target Dr.

free parameters (4)
  • Loss weights alpha_n, alpha_m, alpha_clip, alpha_d, alpha_s = 0.07, 0.3, 1.0, 0.01, 0.5
    Hand-set in Eq. (9); no sensitivity analysis or grid search is reported.
  • Normal-dot threshold for Ld = 0.9
    Pixels with N_d^T N_hat < 0.9 are used to compute Ld; the threshold is chosen ad hoc.
  • Top-k area selection for Ls = 3
    Ls smooths only the top-3 largest SAM masks per view; chosen by hand to avoid over-smoothing small objects.
  • CLIP feature latent dimension = 16
    Inherited from LangSplat; reduces 512-d CLIP features to 16-d per-Gaussian features for efficient storage.
assumptions (4)
  • domain assumption The DSINE monocular normal prior is accurate and view-consistent in indoor scenes.
    Ln uses N_hat to regularize rendered normals (Eq. 4), and Ld uses N_hat to approximate the ideal normal n' (Sec. 4.3). If N_hat is noisy or inconsistent, both losses can distort geometry.
  • domain assumption DEVA masks and LangSplat CLIP features are reliable pseudo-labels for Gaussian semantic features.
    Lm and Lclip treat these 2D predictions as ground truth. The paper itself shows noisy DEVA masks in Fig. 13 but still uses them as supervision, so the method assumes the noise is benign enough not to corrupt geometry.
  • ad hoc to paper The three-case partition of the ideal normal n' is exhaustive and the corresponding depth intervals are correct.
    Sec. 4.3 defines three conditions and Eq. (7) constructs Dr from them, but no proof covers normals with cos(theta') <= 0 or normals outside the assumed plane in Fig. 4.
  • domain assumption PGSR's unbiased depth Dp = D/cos(theta) is a valid per-pixel depth estimate.
    Ld and TSDF fusion both consume Dp; this relation is inherited from PGSR [10] without revalidation on the test scenes.

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Cite this review

Pith. "Pith review of GLS: Geometry-aware 3D Language Gaussian Splatting." pith.science (2026). https://pith.science/paper/G4J3ZILC

@misc{pith2026241118066,
  author       = {Pith},
  title        = {Pith review of: GLS: Geometry-aware 3D Language Gaussian Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G4J3ZILC}},
  note         = {Machine review of arXiv:2411.18066}
}
read the original abstract

Recently, 3D Gaussian Splatting (3DGS) has achieved impressive performance on indoor surface reconstruction and 3D open-vocabulary segmentation. This paper presents GLS, a unified framework of 3D surface reconstruction and open-vocabulary segmentation based on 3DGS. GLS extends two fields by improving their sharpness and smoothness. For indoor surface reconstruction, we introduce surface normal prior as a geometric cue to guide the rendered normal, and use the normal error to optimize the rendered depth. For 3D open-vocabulary segmentation, we employ 2D CLIP features to guide instance features and enhance the surface smoothness, then utilize DEVA masks to maintain their view consistency. Extensive experiments demonstrate the effectiveness of jointly optimizing surface reconstruction and 3D open-vocabulary segmentation, where GLS surpasses state-of-the-art approaches of each task on MuSHRoom, ScanNet++ and LERF-OVS datasets. Project webpage: https://jiaxiongq.github.io/GLS_ProjectPage.

Figures

Figures reproduced from arXiv: 2411.18066 by the authors.

Figure 1
Figure 1. Indoor surface reconstruction and 3D open-vocabulary [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Effect of joint optimization between indoor surface re [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of our framework. We adopt geometric and semantic cues produced by generalizable models to jointly strengthen [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Depth refinement guided by the normal error between [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Effect of Ld. The surface normal is estimated from the unbiased depth. Ld faithfully enhances the sharpness and smooth￾ness of the reconstructed surface. have cos(α) = n ′ · −y and cos(θ ′ ) = n · −y. Conse￾quently, we can get n ′ 1 ∈ {n ′ |cos(α)>cos(θ ′ )>0}, n ′ 2 ∈…
Figure 6
Figure 6. Figure 6: Qualitative comparisons of indoor surface reconstruction on MuSHRoom dataset. PGSR generates unstable results by the default [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparisons of indoor surface reconstruction on ScanNet++ dataset. The lighting conditions change violently in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparisons of 3D open-vocabulary seg [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: A demo of GLS + nerfview + GPT-4V. Our tool can find the object that solves the user’s request (bottom) and extract the scene geometry. Reference Ours PGSR [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Qualitative results in a large-scale indoor scene. Our method successfully handles the high-light and dark regions. [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Qualitative results of the ablation study on ‘b20a261fdf’ of ScanNet++ dataset. The performance of GLS degenerates reasonably [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: Effect of different quality of CLIP features. [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 15
Figure 15. Figure 15: Results of indoor surface reconstruction on LERF-OVS [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]
Figure 16
Figure 16. Figure 16: Semantic mesh results of GLS on LERF-OVS [ [PITH_FULL_IMAGE:figures/full_fig_p015_16.png]
Figure 17
Figure 17. Figure 17: Semantic mesh results of GLS on MuSHRoom [ [PITH_FULL_IMAGE:figures/full_fig_p015_17.png]
Figure 18
Figure 18. Figure 18: More open-vocabulary segmentation results of GLS on LERF-OVS [ [PITH_FULL_IMAGE:figures/full_fig_p016_18.png]

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Reference graph

Works this paper leans on

61 extracted references · 38 canonical work pages · cited by 4 Pith papers

  1. [1]

    nerfview: a minimal* web viewer for interactive NeRF ren- dering, 2024. 12

    work page 2024

  2. [2]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ah- mad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 ,

    arXiv

  3. [3]

    Gwangbin Bae and Andrew J. Davison. Rethinking inductive biases for surface normal estimation. In IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) ,

  4. [4]

    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,

  5. [5]

    Barron, Ben Mildenhall, Dor Verbin, Pratul P

    Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. CVPR, 2022

    2022

  6. [6]

    Barron, Ben Mildenhall, Dor Verbin, Pratul P

    Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Zip-nerf: Anti-aliased grid- based neural radiance fields. ICCV, 2023. 2

    2023

  7. [7]

    Zoedepth: Zero-shot trans- fer by combining relative and metric depth

    Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias M ¨uller. Zoedepth: Zero-shot trans- fer by combining relative and metric depth. arXiv preprint arXiv:2302.12288, 2023. 2

    arXiv 2023

  8. [8]

    GSLoc: Visual Localization with 3D Gaussian Splatting

    Kazii Botashev, Vladislav Pyatov, Gonzalo Ferrer, and Sta- matios Lefkimmiatis. Gsloc: Visual localization with 3d gaussian splatting. arXiv preprint arXiv:2410.06165, 2024. 1

    work page Pith review arXiv 2024

Show all 61 references
  1. [9]

    Emerg- ing properties in self-supervised vision transformers

    Mathilde Caron, Hugo Touvron, Ishan Misra, Herv ´e J´egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerg- ing properties in self-supervised vision transformers. In Pro- ceedings of the IEEE/CVF international conference on com- puter vision, pages 9650–9660, 2021. 3

    2021

  2. [10]

    Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction

    Danpeng Chen, Hai Li, Weicai Ye, Yifan Wang, Weijian Xie, Shangjin Zhai, Nan Wang, Haomin Liu, Hujun Bao, and Guofeng Zhang. Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction. arXiv preprint arXiv:2406.06521, 2024. 1, 2, 4, 5, 6, 8, 11, 12

    2024 arXiv

  3. [11]

    Vcr-gaus: View consistent depth-normal regularizer for gaussian surface reconstruction

    Hanlin Chen, Fangyin Wei, Chen Li, Tianxin Huang, Yun- song Wang, and Gim Hee Lee. Vcr-gaus: View consistent depth-normal regularizer for gaussian surface reconstruction. arXiv preprint arXiv:2406.05774, 2024. 2, 12, 14

    2024 arXiv

  4. [12]

    Flow distillation sampling: Regularizing 3d gaussians with pre-trained matching priors

    Lin-Zhuo Chen, Kangjie Liu, Youtian Lin, Zhihao Li, Siyu Zhu, Xun Cao, and Yao Yao. Flow distillation sampling: Regularizing 3d gaussians with pre-trained matching priors. In ICLR, 2025. 2, 5, 6, 11

    2025

  5. [13]

    Mobilenerf: Exploiting the polygon ras- terization pipeline for efficient neural field rendering on mo- bile architectures

    Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and An- drea Tagliasacchi. Mobilenerf: Exploiting the polygon ras- terization pipeline for efficient neural field rendering on mo- bile architectures. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recog...

    2023

  6. [14]

    Tracking anything with de- coupled video segmentation

    Ho Kei Cheng, Seoung Wug Oh, Brian Price, Alexander Schwing, and Joon-Young Lee. Tracking anything with de- coupled video segmentation. In ICCV, 2023. 1, 3, 4

    2023

  7. [15]

    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. 4

    2024

  8. [16]

    Blender - a 3D modelling and rendering package

    Blender Online Community. Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018. 12

    2018

  9. [17]

    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

  10. [18]

    Geowiz- ard: Unleashing the diffusion priors for 3d geometry esti- mation from a single image

    Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long. Geowiz- ard: Unleashing the diffusion priors for 3d geometry esti- mation from a single image. In European Conference on Computer Vision, pages 241–258. Springer, 2025. 2

    2025

  11. [19]

    Accurate, dense, and robust multiview stereopsis

    Yasutaka Furukawa and Jean Ponce. Accurate, dense, and robust multiview stereopsis. IEEE transactions on pattern analysis and machine intelligence , 32(8):1362–1376, 2009. 2

    2009

  12. [20]

    Multi-view reconstruction of highly specular sur- faces in uncontrolled environments

    Clement Godard, Peter Hedman, Wenbin Li, and Gabriel J Brostow. Multi-view reconstruction of highly specular sur- faces in uncontrolled environments. In 2015 International Conference on 3D Vision, pages 19–27. IEEE, 2015

    2015

  13. [21]

    Dif- ferentiable stereopsis: Meshes from multiple views using differentiable rendering

    Shubham Goel, Georgia Gkioxari, and Jitendra Malik. Dif- ferentiable stereopsis: Meshes from multiple views using differentiable rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 8635–8644, 2022. 2

    2022

  14. [22]

    Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering

    Antoine Gu ´edon and Vincent Lepetit. Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5354–5363, 2024. 1, 2

    2024

  15. [23]

    Semantic gaussians: Open-vocabulary scene understanding with 3d gaussian splatting.arXiv preprint arXiv:2403.15624,

    Jun Guo, Xiaojian Ma, Yue Fan, Huaping Liu, and Qing Li. Semantic gaussians: Open-vocabulary scene understanding with 3d gaussian splatting.arXiv preprint arXiv:2403.15624,

    arXiv

  16. [24]

    2d gaussian splatting for geometrically accu- rate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accu- rate radiance fields. In SIGGRAPH 2024 Conference Papers. Association for Computing Machinery, 2024. 1, 2, 4, 5, 6, 8, 12, 13

    2024

  17. [25]

    Clip2point: Transfer clip to point cloud classifica- tion with image-depth pre-training

    Tianyu Huang, Bowen Dong, Yunhan Yang, Xiaoshui Huang, Rynson WH Lau, Wanli Ouyang, and Wangmeng Zuo. Clip2point: Transfer clip to point cloud classifica- tion with image-depth pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 221...

    2023

  18. [26]

    Open- 9 clip, 2021

    Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Han- naneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Open- 9 clip, 2021. If you use this software, please cite it as ...

    2021

  19. [27]

    Vr-gs: a physical dynamics-aware interactive gaussian splatting system in virtual reality

    Ying Jiang, Chang Yu, Tianyi Xie, Xuan Li, Yutao Feng, Huamin Wang, Minchen Li, Henry Lau, Feng Gao, Yin Yang, et al. Vr-gs: a physical dynamics-aware interactive gaussian splatting system in virtual reality. In ACM SIG- GRAPH 2024 Conference Papers, pages 1–1, 2024. 1

    2024

  20. [28]

    Screened poisson sur- face reconstruction

    Michael Kazhdan and Hugues Hoppe. Screened poisson sur- face reconstruction. ACM Transactions on Graphics (ToG), 32(3):1–13, 2013. 1, 2

    2013

  21. [29]

    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 Transactions on Graphics, 42 (4), 2023. 1, 2, 3, 5

    2023

  22. [30]

    Lerf: Language embedded radiance fields

    Justin Kerr, Chung Min Kim, Ken Goldberg, Angjoo Kanazawa, and Matthew Tancik. Lerf: Language embedded radiance fields. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 19729–19739,

  23. [31]

    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. 1, 3, 4

    2023 arXiv

  24. [32]

    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, 36(4), 2017. 11

    2017

  25. [33]

    Analytic-splatting: Anti-aliased 3d gaussian splatting via analytic integration

    Zhihao Liang, Qi Zhang, Wenbo Hu, Ying Feng, Lei Zhu, and Kui Jia. Analytic-splatting: Anti-aliased 3d gaussian splatting via analytic integration. arXiv preprint arXiv:2403.11056, 2024. 2

    2024 arXiv

  26. [34]

    Marching cubes: A high resolution 3d surface construction algorithm

    William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. InSem- inal graphics: pioneering efforts that shaped the field, pages 347–353. 1998. 4, 5

    1998

  27. [35]

    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. 2, 3

    2020

  28. [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. 2

    2022

  29. [37]

    Kinectfusion: Real-time dense surface mapping and track- ing

    Richard A Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J Davison, Pushmeet Kohi, Jamie Shotton, Steve Hodges, and Andrew Fitzgibbon. Kinectfusion: Real-time dense surface mapping and track- ing. In 2011 10th IEEE international symposium on mixed ...

    2011

  30. [38]

    Radiance fields for robotic teleoperation

    Vaishakh Patil and Marco Hutter. Radiance fields for robotic teleoperation. arXiv preprint arXiv:2407.20194, 2024. 1

    2024 arXiv

  31. [39]

    Langsplat: 3d language gaussian splatting

    Minghan Qin, Wanhua Li, Jiawei Zhou, Haoqian Wang, and Hanspeter Pfister. Langsplat: 3d language gaussian splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20051–20060, 2024. 1, 2, 3, 5, 8, 14

    2024

  32. [40]

    Merf: Memory-efficient radiance fields for real- time view synthesis in unbounded scenes.ACM Transactions on Graphics (TOG), 42(4):1–12, 2023

    Christian Reiser, Rick Szeliski, Dor Verbin, Pratul Srini- vasan, Ben Mildenhall, Andreas Geiger, Jon Barron, and Pe- ter Hedman. Merf: Memory-efficient radiance fields for real- time view synthesis in unbounded scenes.ACM Transactions on Graphics (TOG), 42(4):1–12, 2023. 2

    2023

  33. [41]

    Mushroom: Multi-sensor hy- brid room dataset for joint 3d reconstruction and novel view synthesis

    Xuqian Ren, Wenjia Wang, Dingding Cai, Tuuli Tuominen, Juho Kannala, and Esa Rahtu. Mushroom: Multi-sensor hy- brid room dataset for joint 3d reconstruction and novel view synthesis. In Proceedings of the IEEE/CVF Winter Confer- ence on Applications of Computer Vision, pages 4...

  34. [42]

    Ags-mesh: Adaptive gaussian splatting and meshing with geometric priors for indoor room reconstruction using smart- phones

    Xuqian Ren, Matias Turkulainen, Jiepeng Wang, Otto Seiskari, Iaroslav Melekhov, Juho Kannala, and Esa Rahtu. Ags-mesh: Adaptive gaussian splatting and meshing with geometric priors for indoor room reconstruction using smart- phones. In International Conference on 3D Vision (3DV) ,

  35. [43]

    Pixelwise view selection for unstructured multi-view stereo

    Johannes L Sch ¨onberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Nether- lands, October 11-14, 2016, Proceedings, Part III 14, pag...

    2016

  36. [44]

    Language embedded 3d gaussians for open- vocabulary scene understanding

    Jin-Chuan Shi, Miao Wang, Hao-Bin Duan, and Shao- Hua Guan. Language embedded 3d gaussians for open- vocabulary scene understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5333–5343, 2024. 1, 3

    2024

  37. [45]

    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

  38. [46]

    Dn-splatter: Depth and normal priors for gaussian splatting and meshing, 2024

    Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari, Esa Rahtu, and Juho Kannala. Dn-splatter: Depth and normal priors for gaussian splatting and meshing, 2024. 2, 4, 5, 6, 8, 11

    2024

  39. [47]

    Object- compositional neural implicit surfaces

    Qianyi Wu, Xian Liu, Yuedong Chen, Kejie Li, Chuanxia Zheng, Jianfei Cai, and Jianmin Zheng. Object- compositional neural implicit surfaces. In European Con- ference on Computer Vision, 2022. 2

    2022

  40. [48]

    Objectsdf++: Improved object-compositional neural implicit surfaces

    Qianyi Wu, Kaisiyuan Wang, Kejie Li, Jianmin Zheng, and Jianfei Cai. Objectsdf++: Improved object-compositional neural implicit surfaces. In ICCV, 2023. 2

    2023

  41. [49]

    Opengaussian: Towards point-level 3d gaussian-based open vocabulary understanding

    Yanmin Wu, Jiarui Meng, Haijie Li, Chenming Wu, Yahao Shi, Xinhua Cheng, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, et al. Opengaussian: Towards point-level 3d gaussian-based open vocabulary understanding. arXiv preprint arXiv:2406.02058, 2024. 1, 3, 4, 5, 6, 8, 11, 14

    2024 arXiv

  42. [50]

    Ulip-2: Towards scalable multimodal pre-training for 3d understanding

    Le Xue, Ning Yu, Shu Zhang, Artemis Panagopoulou, Jun- nan Li, Roberto Mart´ın-Mart´ın, Jiajun Wu, Caiming Xiong, Ran Xu, Juan Carlos Niebles, et al. Ulip-2: Towards scalable multimodal pre-training for 3d understanding. In Proceed- ings of the IEEE/CVF Conference on Computer ...

    2024

  43. [51]

    Self- supervised implicit 3d reconstruction via rgb-d scans

    Hongji Yang, Jiao Liu, Shaoping Lu, and Bo Ren. Self- supervised implicit 3d reconstruction via rgb-d scans. In 10 2023 IEEE International Conference on Multimedia and Expo (ICME), pages 1115–1120. IEEE, 2023. 2

    2023

  44. [52]

    Gaussian grouping: Segment and edit anything in 3d scenes

    Mingqiao Ye, Martin Danelljan, Fisher Yu, and Lei Ke. Gaussian grouping: Segment and edit anything in 3d scenes. In ECCV, 2024. 1, 3, 5, 6, 8

    2024

  45. [53]

    Absgs: Recovering fine details in 3d gaussian splat- ting

    Zongxin Ye, Wenyu Li, Sidun Liu, Peng Qiao, and Yong Dou. Absgs: Recovering fine details in 3d gaussian splat- ting. In ACM Multimedia 2024, 2024. 5, 14

    2024

  46. [54]

    Scannet++: A high-fidelity dataset of 3d in- door scenes

    Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, and Angela Dai. Scannet++: A high-fidelity dataset of 3d in- door scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 12–22, 2023. 2, 5, 14, 15

    2023

  47. [55]

    Differentiable surface splatting for point-based geometry processing

    Wang Yifan, Felice Serena, Shihao Wu, Cengiz ¨Oztireli, and Olga Sorkine-Hornung. Differentiable surface splatting for point-based geometry processing. ACM Transactions on Graphics (TOG), 38(6):1–14, 2019. 2

    2019

  48. [56]

    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. Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2024. 2

    2024

  49. [57]

    Gaussian opacity fields: Efficient adaptive surface reconstruction in unbounded scenes

    Zehao Yu, Torsten Sattler, and Andreas Geiger. Gaussian opacity fields: Efficient adaptive surface reconstruction in unbounded scenes. ACM Transactions on Graphics , 2024. 1, 2

    2024

  50. [58]

    Rade-gs: Rasterizing depth in gaussian splatting

    Baowen Zhang, Chuan Fang, Rakesh Shrestha, Yixun Liang, Xiaoxiao Long, and Ping Tan. Rade-gs: Rasterizing depth in gaussian splatting. arXiv preprint arXiv:2406.01467, 2024

    2024 arXiv

  51. [59]

    Neu- ral signed distance function inference through splatting 3d gaussians pulled on zero-level set

    Wenyuan Zhang, Yu-Shen Liu, and Zhizhong Han. Neu- ral signed distance function inference through splatting 3d gaussians pulled on zero-level set. In Advances in Neural Information Processing Systems, 2024. 1

    2024

  52. [60]

    Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields

    Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Ze- hao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, and Achuta Kadambi. Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields. InPro- ceedings of the IEEE/CVF Conference on Compu...

    2024

  53. [61]

    I want to make a piece of toast

    Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Ziyao Zeng, Zipeng Qin, Shanghang Zhang, and Peng Gao. Point- clip v2: Prompting clip and gpt for powerful 3d open-world learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2639–2650, 2023...

    2023

Pith tools

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