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REVIEW 4 major objections 5 minor 1 cited by

Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Car-GS reconstructs reflective car surfaces with a mean Chamfer distance of 0.060 on five 3DRealCar scenes by splitting appearance from geometry and masking unreliable normal priors.

desk verdict Plausible integrated method for reflective/transparent car surfaces, but the SOTA claim rests on five hand-picked scenes and one overclaim that contradicts the paper's own table. read the letter →

arxiv 2501.11020 v1 pith:H5L74F4K submitted 2025-01-19 cs.CV

Congcong Li , Jin Wang , Xiaomeng Wang , Xingchen Zhou , Wei Wu , Yuzhi Zhang , Tongyi Cao This is my paper · ORCID
classification cs.CV
keywords 3DGaussiansplattingsurfacereconstructionreflectivesurfacestransparentview-dependentGaussianslearnablehybridopacitynormalpriorsupervision3DRealCar
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 3D car surfaces, which are notoriously hard to reconstruct because of glossy paint and transparent glass, can be recovered accurately by extending 2D Gaussian Splatting with three targeted mechanisms. The stakes matter because current Gaussian-splatting baselines confuse view-dependent reflections and transparent surfaces, producing noisy or missing geometry on cars, and accurate car meshes are directly useful for autonomous-driving simulation, AR/VR, and games. The paper reports the best surface-reconstruction numbers on its five-scene subset of 3DRealCar, with mean Chamfer Distance 0.060, and attributes the gain to view-dependent Gaussians for reflections, a learnable hybrid opacity that decouples geometry from appearance, and a quality-aware normal-prior mask.

What carries the argument

The load-bearing objects are the view-dependent Gaussian (VDG) set, the learnable hybrid opacity (LHO), and the angle-gated normal supervision. VDGs are extra Gaussian primitives initialized from monocular depth and aligned to structure-from-motion, one set per training view, regularized toward low opacity, and omitted at mesh extraction; they isolate specular highlights so the view-shared Gaussians learn only view-independent appearance. LHO gives each shared Gaussian two opacity values, $\alpha$ for RGB and $\alpha_{\mathrm{geo}}$ for geometry, rendered through the same 2DGS volume-splatting equation but with a consistency loss $L_{\mathrm{lho}}$ that keeps the two close enough to avoid degenerate transparent points. The quality-aware module computes the supervision weight as $w_n = 1$ if $\theta > \tau$ and $0$ otherwise, where $\theta$ is the angle between the Gaussian normal and the pixel ray, using pretrained normal predictions as pseudo labels only where they are reliable.

What would settle it

Run the same training and evaluation pipeline on the full 3DRealCar dataset, or on a randomly drawn held-out set of scenes, and report mean Chamfer Distance with per-scene confidence intervals; if the advantage over Gaussian Shader at 0.098 and PGSR at 0.109 shrinks to noise outside the five selected scenes, the state-of-the-art generalization claim is falsified.

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Extended reading notes

Core claim

The central claim is that a Gaussian-splatting reconstruction pipeline can separate the three failure modes of car surfaces and handle each explicitly. View-dependent Gaussians are trained per viewpoint to absorb specular highlights and are discarded at test time. A learnable hybrid opacity gives each 2D Gaussian a geometry-specific opacity for depth and normal rendering distinct from its RGB opacity, so transparent glass can be geometrically solid while visually transparent. A quality-aware supervision module gates normal-prior supervision by the angle between each Gaussian normal and the viewing ray, zeroing the weight when the view is nearly orthogonal to glass. The paper's evidence is the five-scene 3DRealCar evaluation, where Car-GS reaches mean Chamfer Distance 0.060, Accuracy 0.751, and F1 0.764, beating Gaussian Shader at 0.098 CD and PGSR at 0.109 CD, plus ablations showing each module contributes to the gain.

Load-bearing premise

The load-bearing premise is that the five 3DRealCar scenes chosen for reliable scanner ground truth represent how hard car surfaces are overall; if those scenes are unrepresentatively easy or favorable to this method, the reported state-of-the-art numbers do not generalize to the rest of the 2,500-vehicle dataset.

Editorial extensions

If this is right

  • On the five 3DRealCar scenes, Car-GS reaches a mean Chamfer Distance of 0.060, beating Gaussian Shader at 0.098 and PGSR at 0.109, which is the concrete basis for its state-of-the-art surface-reconstruction claim.
  • Because view-dependent Gaussians are discarded at mesh extraction, the reflection-handling machinery adds no cost at inference; the reported training time is 20 minutes per scene, near the 15 minutes of plain 2DGS and much less than Gaussian Shader's 1h40m.
  • The learnable hybrid opacity implies that depth and normal maps can terminate at a transparent surface while RGB still sees through it, directly targeting glass-penetration artifacts in windshields and windows.
  • With quality-aware normal supervision, a general-purpose pretrained normal prior can be used without letting its errors corrupt geometry, particularly in near-orthogonal glass views where the paper says reconstruction errors concentrate.
  • The method also produces usable novel-view synthesis on 3DRealCar and Ref-NeRF Real, so the geometry-focused changes do not eliminate appearance reconstruction.

Reading between the lines

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

  • Extension: the appearance/geometry opacity split is not car-specific; the same learnable hybrid opacity could be tested on other transparent objects such as bottles or building windows, where depth and RGB rays also diverge.
  • Extension: because view-dependent Gaussians are discarded at evaluation, Car-GS removes specular reflections rather than explaining them, so a follow-up that retains the VDG would be needed for relighting or material editing, which the paper does not address.
  • Extension: the angle-based normal mask could be applied to any normal-prior-guided Gaussian reconstruction, not just cars; a cheap transfer test would run the same quality-aware supervision on a general object dataset and compare surface error against methods with unmasked priors.
  • Extension: a testable prediction is that the largest gains over baselines appear exactly in near-orthogonal glass views; a per-view error analysis over the five scenes would confirm or refute the mechanism claimed for the quality-aware supervision module.
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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 / 5 minor

Summary. The paper proposes Car-GS, a 2DGS-based method for 3D car surface reconstruction that addresses reflective and transparent surfaces through three components: view-dependent Gaussians (VDG) that absorb specular highlights per view, a learnable hybrid opacity (LHO) that separates appearance and geometry opacities, and a quality-aware supervision module (QSM) that weighs normal priors from a pretrained model by the angle between the ray and the Gaussian normal. The central empirical claim is state-of-the-art car surface reconstruction on five 3DRealCar scenes, with mean Chamfer Distance 0.060 versus 0.098 for Gaussian Shader and 0.109 for PGSR (Table 1), plus qualitatively better geometry on Ref-NeRF Real scenes.

Significance. If the reported geometry gains hold across the full 3DRealCar distribution, the paper would be a useful contribution to reflective/transparent surface reconstruction, a known weakness of Gaussian splatting methods. The design is reasonably grounded: the VDG/LHO/QSM components are motivated by physically plausible observations about specular highlights and ray termination on glass, and the geometry evaluation uses externally acquired high-precision scanner meshes rather than self-derived targets. The paper also provides a practical training-time advantage (20 minutes versus 1h40m for Gaussian Shader). However, the evidence for the headline SOTA claim currently rests on five hand-picked scenes with no selection rule, no error bars, and no full-dataset or random-subset validation, and the paper's own Table 2 contradicts the accompanying rendering-superiority claim.

major comments (4)
  1. [Section 4.1, Table 1] The 'carefully selected five scenes with reliable ground truth data' sentence is load-bearing for the central SOTA claim, but no selection criterion is given. Since all three contributions target exactly the reflective/transparent regions where scanner GT is hardest to obtain, a selection rule based on GT reliability can select on the outcome, removing the cases where baselines fail most. The paper should state the total number of scenes inspected, the numeric GT-confidence score used for selection, and ideally report results on a random subset and/or the full dataset. Without this, the 0.060 versus 0.098 Chamfer Distance gap is not separable from selection bias.
  2. [Section 4.3, Table 2] The text states 'our approach also demonstrates superior rendering performance,' but Table 2 contradicts this: on 3DRealCar, Ours has PSNR 23.982 versus PGSR 25.818, SSIM 0.803 versus 0.876, and LPIPS 0.258 versus 0.122; on Ref-NeRF, Ours is also not best on any metric. The rendering claim is therefore false as stated. The authors should either remove the rendering-superiority claim or reframe it as competitive rendering, and should not use 'superior' in the abstract or Section 4.3 to describe rendering quality.
  3. [Section 3.3, Eq. (10)] The quality-aware supervision module depends on the threshold tau, but tau is never specified in the paper or in Section 4.2's implementation details. Since QSM is one of the three contributions and the loss mask in Fig. 4 is directly determined by tau, the paper should report the value used, any normalization convention for the angle, and a sensitivity study over tau. Without this, the adaptive supervision is not reproducible.
  4. [Section 4.4, Table 3] The ablation table reports a single set of numbers with no indication of which scene(s) were used. The full-model CD 0.060, Accuracy 0.636, and F1 0.664 exactly match Scene 4 in Table 1, strongly suggesting the ablation was run on one scene only. If that is the case, the paper should say so explicitly and provide per-scene ablation results or at least means and variances over multiple scenes; otherwise the component analysis cannot support a cross-scene robustness claim.
minor comments (5)
  1. [Section 3.1, Eq. (3)] The scale s and offset o for aligning monocular depth with COLMAP are said to be 'obtained following the method in 3DGS [17]', but 3DGS does not perform monocular depth alignment. Please cite the actual alignment procedure or describe the fitting objective.
  2. [Section 3.3 and Table 3 caption] The module is called Quality-aware Supervised Module (QSM) in Section 3.3 and the abstract, but the Table 3 caption abbreviates it as QAS. Please unify the abbreviation throughout.
  3. [Section 3.3, text near Eq. (9)] The sentence 'leverages ground truth normals from a pre-trained normal estimation model' is misleading because these are pseudo labels, not ground truth. Please replace 'ground truth' with 'pseudo' or 'predicted' normals.
  4. [Section 4.1] The Ref-NeRF Real dataset has no ground truth geometry, so the comparison there is only visual. Please state this limitation explicitly and avoid wording like 'consistently yields geometrically accurate surface reconstructions' without quantitative support on that dataset.
  5. [Figure 4 caption] The caption says 'black indicates a value of 0, while white indicates a value of 1' for the mask, but Eq. (10) sets wn=0 for the orthogonal regions that are described as errors. Please confirm whether these regions are black in Fig. 4(c) and clarify the color mapping.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SOTA claim is an external empirical comparison, not a derivation from fitted inputs or self-citations.

full rationale

The paper's main claim is an empirical reconstruction-quality comparison on two external datasets (3DRealCar and Ref-NeRF Real). Ground-truth meshes come from a high-precision 3D scanner, baselines are prior published methods, and the normal prior is the pretrained StableNormal model; no evaluation metric (Chamfer Distance, Accuracy, F1) appears as a fitting term or is defined in terms of the method's components. The three contributions are architectural and loss-level changes: VDG are trained to absorb specular highlights and then discarded at evaluation, LHO introduces a separate geometry opacity constrained to stay close to appearance opacity, and QSM gates normal supervision by a fixed angle threshold. None of these is defined by the target result or by a quantity computed from the submitted meshes. The one in-manuscript limitation that deserves a flag is Section 4.1's statement that the authors 'carefully selected five scenes with reliable ground truth data' from 3DRealCar; this is a potential selection-bias threat to the generality of the 0.060 CD number, and Table 3's full-model scores (0.060/0.636/0.664) exactly match Scene 4 of Table 1, suggesting the ablation may be single-scene. These are robustness and reporting concerns, not circularity: the selected scenes and metrics are not generated by the method, and the score is not forced by construction. No load-bearing self-citation or renamed-known-result pattern is present, so the correct circularity finding is no circularity.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The paper introduces new method components (view-dependent Gaussians, geometry-specific opacity, quality-aware mask) but no new physical or conceptual entities beyond these parameters. All quantitative claims rest on fitted loss weights, an unreported threshold, and a hand-picked evaluation subset.

free parameters (5)
  • Normal-supervision angle threshold tau = not reported
    Used in Eq. (10) to zero out normal supervision when the angle between the normal and the ray is small. The value is never given and no sensitivity analysis is presented.
  • Loss weights wvdg, wlho, wds, wn = 0.2, 3.0, 0.1, 0.1
    Weights for the VDG sparsity, hybrid-opacity consistency, depth-normal, and normal losses, set in Section 3.4. They are chosen by the authors on the evaluation data and are not ablated.
  • VDG count per view = 10,000
    Fixed number of view-dependent Gaussians per camera (Section 4.2). This is a design choice that affects capacity and training time.
  • Monocular-depth scale s and offset o = per scene, from 3DGS alignment
    Eq. (3) aligns Depth Anything V2 relative depth to COLMAP scale. The alignment is fit per scene and directly determines VDG initialization positions.
  • TSDF voxel size and truncation threshold = 0.004, 0.02
    Mesh extraction parameters (Section 4.2) that directly affect the final geometry metrics and are not ablated.
assumptions (6)
  • domain assumption Gaussian splatting with 2DGS provides an adequate surface representation for car bodies including glass.
    The whole method operates within the 2DGS framework; Section 3 states the approach is based on 2DGS [14].
  • ad hoc to paper Specular reflection can be decomposed into view-dependent and view-shared components, and the view-dependent component can be absorbed by per-view Gaussians without affecting geometry.
    Section 3.1 posits that VDG fit specular highlights and are discarded at test time. If VDG also absorb diffuse or occlusion information, the shared geometry would be biased.
  • ad hoc to paper Depth rays and appearance rays should terminate differently on transparent surfaces, and separate opacity parameters can capture this without breaking multi-view consistency.
    Section 3.2's LHO design depends on this assumption, and the Llho consistency loss is introduced to prevent degenerate solutions, indicating the assumption needs extra constraints.
  • ad hoc to paper StableNormal predictions are reliable except when the view ray is nearly orthogonal to the surface, and the angle threshold tau identifies these failures.
    Section 3.3 and Eq. (10) rely on this correlation, but the paper supports it only with the qualitative example in Fig. 4 and never specifies tau.
  • domain assumption Monocular depth from Depth Anything V2, after scale alignment, provides a valid initialization for VDG on textureless regions.
    Section 3.1 uses Eq. (3) to convert monocular depth into VDG positions. If the monocular depth is wrong, VDG initialize in incorrect locations.
  • domain assumption The selected five scenes' ground truth from the high-precision scanner is reliable and representative of the car reconstruction problem.
    Section 4.1 explicitly selects scenes with reliable ground truth and does not evaluate on the full dataset, so representativeness is assumed.

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Cite this review

Pith. "Pith review of Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction." pith.science (2026). https://pith.science/paper/H5L74F4K

@misc{pith2026250111020,
  author       = {Pith},
  title        = {Pith review of: Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H5L74F4K}},
  note         = {Machine review of arXiv:2501.11020}
}
read the original abstract

3D car modeling is crucial for applications in autonomous driving systems, virtual and augmented reality, and gaming. However, due to the distinctive properties of cars, such as highly reflective and transparent surface materials, existing methods often struggle to achieve accurate 3D car reconstruction.To address these limitations, we propose Car-GS, a novel approach designed to mitigate the effects of specular highlights and the coupling of RGB and geometry in 3D geometric and shading reconstruction (3DGS). Our method incorporates three key innovations: First, we introduce view-dependent Gaussian primitives to effectively model surface reflections. Second, we identify the limitations of using a shared opacity parameter for both image rendering and geometric attributes when modeling transparent objects. To overcome this, we assign a learnable geometry-specific opacity to each 2D Gaussian primitive, dedicated solely to rendering depth and normals. Third, we observe that reconstruction errors are most prominent when the camera view is nearly orthogonal to glass surfaces. To address this issue, we develop a quality-aware supervision module that adaptively leverages normal priors from a pre-trained large-scale normal model.Experimental results demonstrate that Car-GS achieves precise reconstruction of car surfaces and significantly outperforms prior methods. The project page is available at https://lcc815.github.io/Car-GS.

Figures

Figures reproduced from arXiv: 2501.11020 by the authors.

Figure 1
Figure 1. Comparison of various methods based on training time [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Our Car-GS method accurately separates reflections and recovers surface normals, achieves photo-realistic rendering, and demon [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of Car-GS. We initialize View-Dependent Gaussians (VDG) and View-Shared Gaussians (VSG) using monocular depth estimates aligned with structure-from-motion (SfM). VDG models view-specific attributes, while VSG captures shared information. During rendering, a learnable hybrid opacity is applied to RGB images and depth/normal maps. Additionally, a quality-aware supervision module leverages pretrained normal pr… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: (a), (b), and (c) represent the input image, the normal [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Visual comparisons on test-set views from the 3DRealCar dataset. Note that we focus on the reconstruction of the vehicle [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Visual comparisons on test-set views from the Ref-Nerf real dataset. Our method achieves superior normal estimations, particu [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Rendered normal maps and reconstructed meshes on [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

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Forward citations

Cited by 1 Pith paper

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  1. TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors

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    TSGS decouples geometry from appearance in Gaussian splatting and extracts first-surface depth via a transmittance sliding window, improving transparent surface reconstruction on a new synthetic lab-glass benchmark.

Reference graph

Works this paper leans on

48 extracted references · 30 canonical work pages · cited by 1 Pith paper

  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. 1

    2022

  3. [3]

    Depth esti- mation from indoor panoramas with neural scene representa- tion

    Wenjie Chang, Yueyi Zhang, and Zhiwei Xiong. Depth esti- mation from indoor panoramas with neural scene representa- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 899–908, 2023. 2

    work page 2023

  4. [4]

    Deep surface light fields.Pro- ceedings of the ACM on Computer Graphics and Interactive Techniques, 1(1):1–17, 2018

    Anpei Chen, Minye Wu, Yingliang Zhang, Nianyi Li, Jie Lu, Shenghua Gao, and Jingyi Yu. Deep surface light fields.Pro- ceedings of the ACM on Computer Graphics and Interactive Techniques, 1(1):1–17, 2018. 1

    work page 2018

  5. [5]

    Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction, 2024

    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, 2024. 3

    work page 2024

  6. [6]

    Uc-nerf: Neural radiance field for under-calibrated multi-view cameras in autonomous driving

    Kai Cheng, Xiaoxiao Long, Wei Yin, Jin Wang, Zhiqiang Wu, Yuexin Ma, Kaixuan Wang, Xiaozhi Chen, and Xuejin Chen. Uc-nerf: Neural radiance field for under-calibrated multi-view cameras in autonomous driving. arXiv preprint arXiv:2311.16945, 2023. 2

    arXiv 2023

  7. [7]

    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

  8. [8]

    High-quality surface recon- struction using gaussian surfels

    Pinxuan Dai, Jiamin Xu, Wenxiang Xie, Xinguo Liu, Huamin Wang, and Weiwei Xu. High-quality surface recon- struction using gaussian surfels. In ACM SIGGRAPH 2024 Conference Papers. Association for Computing Machinery,

    work page 2024

Show all 48 references
  1. [9]

    3drealcar: An in-the-wild rgb- d car dataset with 360-degree views

    Xiaobiao Du, Haiyang Sun, Shuyun Wang, Zhuojie Wu, Hongwei Sheng, Jiaying Ying, Ming Lu, Tianqing Zhu, Kun Zhan, and Xin Yu. 3drealcar: An in-the-wild rgb- d car dataset with 360-degree views. arXiv preprint arXiv:2406.04875, 2024. 7

    2024 arXiv

  2. [10]

    Tensorir: An abstraction for automatic tensorized program optimization

    Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin, Junru Shao, Ruihang Lai, Zihao Ye, Lianmin Zheng, Cody Hao Yu, Yong Yu, et al. Tensorir: An abstraction for automatic tensorized program optimization. In Proceedings of the 28th ACM International Conference on Architectural Suppor...

    2023

  3. [11]

    Ref-neus: Ambiguity-reduced neural implicit surface learning for multi-view reconstruction with reflection

    Wenhang Ge, Tao Hu, Haoyu Zhao, Shu Liu, and Ying-Cong Chen. Ref-neus: Ambiguity-reduced neural implicit surface learning for multi-view reconstruction with reflection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4251–4260, 2023. 4

    2023

  4. [12]

    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. CVPR, 2024. 3

    2024

  5. [13]

    Tri-miprf: Tri-mip represen- tation for efficient anti-aliasing neural radiance fields

    Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma. Tri-miprf: Tri-mip represen- tation for efficient anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19774–19783, 2023. 2

    2023

  6. [14]

    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. 3, 4

    2024

  7. [15]

    2d gaussian splatting for geometrically ac- curate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically ac- curate radiance fields. In ACM SIGGRAPH 2024 conference papers, pages 1–11, 2024. 3

    2024

  8. [16]

    Gaussian- shader: 3d gaussian splatting with shading functions for re- flective surfaces

    Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xi- aoxiao Long, Wenping Wang, and Yuexin Ma. Gaussian- shader: 3d gaussian splatting with shading functions for re- flective surfaces. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition , p...

    2024

  9. [17]

    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,

  10. [18]

    V ox-surf: V oxel-based implicit sur- face representation

    Hai Li, Xingrui Yang, Hongjia Zhai, Yuqian Liu, Hujun Bao, and Guofeng Zhang. V ox-surf: V oxel-based implicit sur- face representation. IEEE Transactions on Visualization and Computer Graphics, 2022. 3

    2022

  11. [19]

    Neuralangelo: High-fidelity neural surface reconstruction

    Zhaoshuo Li, Thomas M ¨uller, Alex Evans, Russell H Tay- lor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin. Neuralangelo: High-fidelity neural surface reconstruction. In IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2023. 3

    2023

  12. [20]

    Rip-nerf: Anti-aliasing radiance fields with ripmap-encoded platonic solids

    Junchen Liu, Wenbo Hu, Zhuo Yang, Jianteng Chen, Guo- liang Wang, Xiaoxue Chen, Yantong Cai, Huan-ang Gao, and Hao Zhao. Rip-nerf: Anti-aliasing radiance fields with ripmap-encoded platonic solids. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 2

    2024

  13. [21]

    Neural sparse voxel fields

    Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. NeurIPS,

  14. [22]

    Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images

    Yuan Liu, Peng Wang, Cheng Lin, Xiaoxiao Long, Jiepeng Wang, Lingjie Liu, Taku Komura, and Wenping Wang. Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images. ACM Transactions on Graphics (TOG), 42(4):1–22, 2023. 3

    2023

  15. [23]

    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

  16. [24]

    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, 3 10

    2021

  17. [25]

    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

  18. [26]

    Differentiable volumetric rendering: Learn- ing implicit 3d representations without 3d supervision, 2020

    Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. Differentiable volumetric rendering: Learn- ing implicit 3d representations without 3d supervision, 2020. 3

    2020

  19. [27]

    Sam 2: Segment anything in images and videos

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R¨adle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junt- ing Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Doll´ar, and Christoph Feic...

    2024 arXiv

  20. [28]

    Octree-gs: Towards consistent real-time rendering with lod-structured 3d gaussians

    Kerui Ren, Lihan Jiang, Tao Lu, Mulin Yu, Linning Xu, Zhangkai Ni, and Bo Dai. Octree-gs: Towards consistent real-time rendering with lod-structured 3d gaussians. arXiv preprint arXiv:2403.17898, 2024. 2

    2024 arXiv

  21. [29]

    Mega-nerf: Scalable construction of large- scale nerfs for virtual fly-throughs

    Haithem Turki, Deva Ramanan, and Mahadev Satya- narayanan. Mega-nerf: Scalable construction of large- scale nerfs for virtual fly-throughs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12922–12931, 2022. 2

    2022

  22. [30]

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

    Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari, Esa Rahtu, and Juho Kannala. Dn-splatter: Depth and normal priors for gaussian splatting and meshing. In Proceedings of the IEEE/CVF Winter Conference on Appli- cations of Computer Vision (WACV), 2025. 3

    2025

  23. [31]

    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

  24. [32]

    Nerf-casting: Improved view-dependent appearance with consistent reflections

    Dor Verbin, Pratul P Srinivasan, Peter Hedman, Ben Milden- hall, Benjamin Attal, Richard Szeliski, and Jonathan T Bar- ron. Nerf-casting: Improved view-dependent appearance with consistent reflections. In SIGGRAPH Asia 2024 Con- ference Papers, pages 1–10, 2024. 3

    2024

  25. [33]

    Unisdf: Unifying neural representations for high- fidelity 3d reconstruction of complex scenes with reflections

    Fangjinhua Wang, Marie-Julie Rakotosaona, Michael Niemeyer, Richard Szeliski, Marc Pollefeys, and Federico Tombari. Unisdf: Unifying neural representations for high- fidelity 3d reconstruction of complex scenes with reflections. arXiv preprint arXiv:2312.13285, 2023. 4

    2023 arXiv

  26. [34]

    Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction

    Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689, 2021. 1, 3

    2021 arXiv

  27. [35]

    GS2Mesh: Surface reconstruction from Gaussian splatting via novel stereo views

    Yaniv Wolf, Amit Bracha, and Ron Kimmel. GS2Mesh: Surface reconstruction from Gaussian splatting via novel stereo views. In European Conference on Computer Vision (ECCV), 2024. 3

    2024

  28. [36]

    Surface light fields for 3d photography

    Daniel N Wood, Daniel I Azuma, Ken Aldinger, Brian Cur- less, Tom Duchamp, David H Salesin, and Werner Stuetzle. Surface light fields for 3d photography. InSeminal Graphics Papers: Pushing the Boundaries, Volume 2, pages 487–496

  29. [37]

    Neural directional encoding for efficient and accurate view-dependent appearance modeling

    Liwen Wu, Sai Bi, Zexiang Xu, Fujun Luan, Kai Zhang, Iliyan Georgiev, Kalyan Sunkavalli, and Ravi Ramamoor- thi. Neural directional encoding for efficient and accurate view-dependent appearance modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ...

    2024

  30. [38]

    Point- nerf: Point-based neural radiance fields

    Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, and Ulrich Neumann. Point- nerf: Point-based neural radiance fields. arXiv preprint arXiv:2201.08845, 2022. 3

    2022 arXiv

  31. [39]

    Depth any- thing v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiao- gang Xu, Jiashi Feng, and Hengshuang Zhao. Depth any- thing v2. arXiv:2406.09414, 2024. 4

    2024 arXiv

  32. [40]

    Spec-gaussian: Anisotropic view-dependent appearance for 3d gaussian splatting

    Ziyi Yang, Xinyu Gao, Yangtian Sun, Yihua Huang, Xi- aoyang Lyu, Wen Zhou, Shaohui Jiao, Xiaojuan Qi, and Xiaogang Jin. Spec-gaussian: Anisotropic view-dependent appearance for 3d gaussian splatting. arXiv preprint arXiv:2402.15870, 2024. 2

    2024 arXiv

  33. [41]

    V ol- ume rendering of neural implicit surfaces, 2021

    Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. V ol- ume rendering of neural implicit surfaces, 2021. 3

    2021

  34. [42]

    Gaustudio: A modular frame- work for 3d gaussian splatting and beyond

    Chongjie Ye, Yinyu Nie, Jiahao Chang, Yuantao Chen, Yi- hao Zhi, and Xiaoguang Han. Gaustudio: A modular frame- work for 3d gaussian splatting and beyond. arXiv preprint arXiv:2403.19632, 2024. 3

    2024 arXiv

  35. [43]

    Stablenormal: Reducing diffusion variance for stable and sharp normal

    Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han. Stablenormal: Reducing diffusion variance for stable and sharp normal. ACM Transactions on Graphics (TOG), 43(6):1–18, 2024. 2, 4, 6

    2024

  36. [44]

    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 (proceedings of ACM SIGGRAPH ASIA) , 38(6),

  37. [45]

    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. 3

    2024

  38. [46]

    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 (TOG), 43(6):1–13, 2024. 2

    2024

  39. [47]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 586–595, 2018. 7

    2018

  40. [48]

    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. 3 11

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.