pith. sign in

arxiv: 2606.21898 · v1 · pith:KKTRB222new · submitted 2026-06-20 · 💻 cs.GR · cs.CV

Mesh2GS: White-Box 3DGS Construction via Plenoptic Sampling

Haoran Zhu , Youcheng Cai , Huangsheng Du , Jingyang Meng , Ligang Liu This is my paper

Pith reviewed 2026-06-26 11:09 UTC · model grok-4.3

classification 💻 cs.GR cs.CV
keywords 3D Gaussian Splattingmesh conversionplenoptic samplingglobal illuminationwhite-box constructionNyquist ratenon-Lambertian effects
0
0 comments X

The pith

Mesh2GS converts a mesh into 3D Gaussian Splatting by deriving view count and Gaussian placement from plenoptic sampling theory.

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

The paper seeks to replace heuristic binding of Gaussians to mesh surfaces with a direct construction method grounded in sampling theory. It derives the minimum number of views needed and the spatial distribution of the Gaussians so that the resulting 3DGS representation meets the Nyquist criterion for global illumination. Additional steps decompose albedo from shading and add a neural module to capture view-dependent effects. A sympathetic reader would care because the method supplies an explicit link between two common 3D representations rather than treating the conversion as an opaque optimization problem.

Core claim

Mesh2GS generates 3DGS directly from mesh geometry based on plenoptic sampling theory, achieving Nyquist-level performance for high-quality global illumination rendering.

What carries the argument

plenoptic sampling guided 3DGS construction strategy that theoretically derives the minimum sampling rate of the sampled views and the distribution of 3D Gaussians

Load-bearing premise

That plenoptic sampling theory directly yields the minimum sampling rate of views and the distribution of 3D Gaussians when applied to mesh-to-3DGS conversion.

What would settle it

A controlled scene where the number of views or Gaussian density predicted by the method produces visible artifacts while a different density or view count yields artifact-free results at the same computational budget.

Figures

Figures reproduced from arXiv: 2606.21898 by Haoran Zhu, Huangsheng Du, Jingyang Meng, Ligang Liu, Youcheng Cai.

Figure 1
Figure 1. Figure 1: Overview of our white-box 3DGS construction framework. Given scene geome￾try, we first explicitly construct 3DGS based on plenoptic sampling theory for reliable caching of global illumination effects. Subsequently, the 3DGS update procedure utilizes path-traced renderings to efficiently update 3DGS and capture global illumination. 3 Overview An overview of our framework is shown in [PITH_FULL_IMAGE:figure… view at source ↗
Figure 2
Figure 2. Figure 2: Left: Sampling interval of 3D Gaussians ∆w in world space based. Mid: 3D Gaussians should be distributed just below the visible surfaces of scenes. Right: The 3DGS can naturally represent numerous depth ranges, rendering images at different depth layers. on the sampling interval ∆v. Therefore, we propose a white-box 3DGS construction strategy guided by plenoptic sampling theory [2]. Our approach explicitly… view at source ↗
Figure 3
Figure 3. Figure 3: Our 3DGS update procedure employs path-traced renderings from the sam￾pled views to optimize the color attributes of the 3D Gaussians. The albedo-shading decomposition strategy is employed to improve performance. 4.3 Neural Illumination Enhancement Although 3D Gaussian Splatting (3DGS) efficiently represents scene geometry and base appearance, its explicit Gaussian representation is limited in model￾ing co… view at source ↗
read the original abstract

3D Gaussian Splatting (3DGS) has emerged as a promising method for high-quality, real-time 3D reconstruction. To associate 3DGS with mesh representations, existing methods primarily focus on 3DGS-to-mesh reconstruction from multi-view images. In contrast, the problem of converting a mesh into 3DGS has received comparatively less attention. Instead of relying on heuristic strategies that bind 3D Gaussians to the mesh, we propose a novel white-box 3DGS construction framework, termed Mesh2GS, which generates 3DGS directly from mesh geometry based on plenoptic sampling theory, achieving Nyquist-level performance for high-quality global illumination rendering. Firstly, we propose a plenoptic sampling guided 3DGS construction strategy that theoretically derives the minimum sampling rate of the sampled views and the distribution of 3D Gaussians. Second, we propose a novel 3DGS update procedure with albedo--shading decomposition for efficient global-illumination capture. Finally, we introduce a neural illumination enhancement module to handle non-Lambertian effects. Experimental results demonstrate that our method surpasses state-of-the-art baselines and is practically effective for both real-time shared rendering and non-Lambertian effects capturing specular highlights. The project code will be released upon acceptance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 0 minor

Summary. The paper claims to present Mesh2GS, a white-box framework that constructs 3D Gaussian Splatting directly from mesh geometry via plenoptic sampling theory. It derives the minimum view sampling rate and 3D Gaussian distribution, proposes an albedo-shading decomposition update for global illumination, and adds a neural module for non-Lambertian effects. Experiments are reported to show superiority over baselines for real-time rendering and specular highlight capture.

Significance. If the central theoretical derivation holds, the work would supply a principled, non-heuristic bridge between mesh and 3DGS representations, supporting higher-quality global illumination without ad-hoc Gaussian binding. The planned code release would further strengthen reproducibility.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and for recognizing the potential value of a principled, non-heuristic connection between mesh geometry and 3D Gaussian Splatting. The recommendation of 'uncertain' appears to hinge on verification of the central theoretical claims; we are prepared to strengthen the presentation of those derivations if the referee can indicate specific points of concern. No major comments were enumerated in the report, so we provide no point-by-point responses below.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and context contain no equations, fitting procedures, or self-citations that could be inspected for reduction to inputs by construction. The central claim of deriving minimum sampling rates and 3D Gaussian distributions from plenoptic sampling theory is presented as a first-principles result without visible internal definitions or renamings that equate outputs to inputs. No load-bearing steps matching the enumerated circularity patterns are identifiable from the given text. The derivation is therefore treated as self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5777 in / 949 out tokens · 18757 ms · 2026-06-26T11:09:35.299593+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

35 extracted references · 1 canonical work pages

  1. [1]

    Proceedings of the ACM in Computer Graphics Interactive Techniques3(2), 15 (2020)

    Andersson, P., Akenine-Möller, T., Nilsson, J., Åström, K., Oskarsson, M., Fairchild, M.: Flip: A difference evaluator for alternating images. Proceedings of the ACM in Computer Graphics Interactive Techniques3(2), 15 (2020)

    2020

  2. [2]

    In: Conference on Computer Graphics and Interactive Techniques

    Chai, J.X., Tong, X., Chan, S.C., Shum, H.Y.: Plenoptic sampling. In: Conference on Computer Graphics and Interactive Techniques. pp. 307–318 (2000)

    2000

  3. [3]

    In: Asian Conference on Computer Vision

    Choi, J., Lee, Y., Lee, H., Kwon, H., Manocha, D.: Meshgs: Adaptive mesh-aligned gaussian splatting for high-quality rendering. In: Asian Conference on Computer Vision. pp. 3310–3326 (2024)

    2024

  4. [4]

    IEEE Transactions on Visualization Computer Graphics (2024)

    Fei, B., Xu, J., Zhang, R., Zhou, Q., Yang, W., He, Y.: 3d gaussian splatting as new era: A survey. IEEE Transactions on Visualization Computer Graphics (2024)

    2024

  5. [5]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Flynn, J., Neulander, I., Philbin, J., Snavely, N.: Deepstereo: Learning to predict new views from the world’s imagery. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5515–5524 (2016)

    2016

  6. [6]

    arXiv preprint arXiv:.17811 (2024)

    Gao, X., Li, X., Zhuang, Y., Zhang, Q., Hu, W., Zhang, C., Yao, Y., Shan, Y., Quan, L.: Mani-gs: Gaussian splatting manipulation with triangular mesh. arXiv preprint arXiv:.17811 (2024)

    2024

  7. [7]

    In: Con- ference on Computer Graphics and Interactive Techniques

    Gortler, S.J., Grzeszczuk, R., Szeliski, R., Cohen, M.F.: The lumigraph. In: Con- ference on Computer Graphics and Interactive Techniques. p. 43–54 (1996)

    1996

  8. [8]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Guédon, A., Lepetit, V.: Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5354–5363 (2024)

    2024

  9. [10]

    In: ACM SIGGRAPH Conference

    Huang, B., Yu, Z., Chen, A., Geiger, A., Gao, S.: 2d gaussian splatting for ge- ometrically accurate radiance fields. In: ACM SIGGRAPH Conference. pp. 1–11 (2024)

    2024

  10. [11]

    In: Conference on Computer Graphics and Interactive Techniques

    Kajiya, J.T.: The rendering equation. In: Conference on Computer Graphics and Interactive Techniques. pp. 143–150 (1986)

    1986

  11. [12]

    ACM Transactions on Graphics35(6), 1–10 (2016)

    Kalantari, N.K., Wang, T.C., Ramamoorthi, R.: Learning-based view synthesis for light field cameras. ACM Transactions on Graphics35(6), 1–10 (2016)

    2016

  12. [13]

    Physically Based Shading Theory Practice4(3), 1 (2013)

    Karis, B., Games, E.: Real shading in unreal engine 4. Physically Based Shading Theory Practice4(3), 1 (2013)

    2013

  13. [14]

    In: Eurographics Symposium on Rendering

    Kautz, J., Lehtinen, J., Aila, T.: Hemispherical rasterization for self-shadowing of dynamic objects. In: Eurographics Symposium on Rendering. p. 179–184 (2004)

    2004

  14. [15]

    ACM Transactions on Graphics42(4), 139:1– 139:14 (2023)

    Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics42(4), 139:1– 139:14 (2023)

    2023

  15. [16]

    In: ACM SIGGRAPH Conference

    Kristensen, A.W., Akenine-Möller, T., Jensen, H.W.: Precomputed local radiance transfer for real-time lighting design. In: ACM SIGGRAPH Conference. pp. 1208–

  16. [17]

    In: Rendering Techniques’96

    Lafortune, E.P., Willems, Y.D.: Rendering participating media with bidirectional path tracing. In: Rendering Techniques’96. pp. 91–100 (1996)

    1996

  17. [18]

    In: Conference on Computer Graph- ics and Interactive Techniques

    Levoy, M., Hanrahan, P.: Light field rendering. In: Conference on Computer Graph- ics and Interactive Techniques. p. 31–42 (1996)

    1996

  18. [19]

    In: Findings of the Asso- ciation for Computational Linguistics

    Lv, X., Zhang, P., Li, S., Gan, G., Sun, Y.: Lightformer: Light-weight transformer using svd-based weight transfer and parameter sharing. In: Findings of the Asso- ciation for Computational Linguistics. pp. 10323–10335 (2023) 16 F. Author et al

    2023

  19. [20]

    Journal of Computer Graphics Techniques8(2) (2019)

    Majercik, Z., Guertin, J.P., Nowrouzezahrai, D., McGuire, M.: Dynamic diffuse global illumination with ray-traced irradiance fields. Journal of Computer Graphics Techniques8(2) (2019)

    2019

  20. [21]

    Computer Graphics Forum41(1), 158–171 (2021)

    Majercik, Z., Müller, T., Keller, A., Nowrouzezahrai, D., McGuire, M.: Dynamic diffuse global illumination resampling. Computer Graphics Forum41(1), 158–171 (2021)

    2021

  21. [22]

    ACM Transactions on Graphics38(4), 1–14 (2019)

    Mildenhall, B., Srinivasan, P.P., Ortiz-Cayon, R., Kalantari, N.K., Ramamoorthi, R., Ng, R., Kar, A.: Local light field fusion: Practical view synthesis with prescrip- tive sampling guidelines. ACM Transactions on Graphics38(4), 1–14 (2019)

    2019

  22. [23]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Mitra, K., Veeraraghavan, A.: Light field denoising, light field superresolution and stereo camera based refocussing using a gmm light field patch prior. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22–28 (2012)

    2012

  23. [24]

    arXiv preprint arXiv:.12372 (2021)

    Müller, T., Rousselle, F., Novák, J., Keller, A.: Real-time neural radiance caching for path tracing. arXiv preprint arXiv:.12372 (2021)

    2021

  24. [25]

    nvidia.com/optix-denoiser, online; accessed 2017

    Research, N.: Nvidia optix ai-accelerated denoiser (2017),https://developer. nvidia.com/optix-denoiser, online; accessed 2017

    2017

  25. [26]

    ACM Transactions on Graphics21(3), 527–536 (2002)

    Sloan, P.P., Kautz, J., Snyder, J.: Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. ACM Transactions on Graphics21(3), 527–536 (2002)

    2002

  26. [27]

    In: International Conference on Computer Vision

    Srinivasan, P.P., Wang, T., Sreelal, A., Ramamoorthi, R., Ng, R.: Learning to synthesize a 4d rgbd light field from a single image. In: International Conference on Computer Vision. pp. 2243–2251 (2017)

    2017

  27. [28]

    In: Conference on Computer Graphics and Interactive Techniques

    Veach, E., Guibas, L.J.: Optimally combining sampling techniques for monte carlo rendering. In: Conference on Computer Graphics and Interactive Techniques. pp. 419–428 (1995)

    1995

  28. [29]

    Games: Mesh-based adapting and modification of gaussian splatting,

    Waczyńska, J., Borycki, P., Tadeja, S., Tabor, J., Spurek, P.: Games: Mesh-based adapting and modification of gaussian splatting. arXiv preprint arXiv:2402.01459 (2024)

    work page arXiv 2024

  29. [30]

    IEEE transactions on image processing 13(4), 600–612 (2004)

    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004)

    2004

  30. [31]

    In: ACM SIGGRAPH Courses (2022)

    Wright, D., Narkowicz, K., Kelly, P.: Lumen: Real-time global illumination in un- real engine 5. In: ACM SIGGRAPH Courses (2022)

    2022

  31. [32]

    IEEE Transactions on Circuits Systems for Video Technology13(11), 1038–1050 (2003)

    Zhang, C., Chen, T.: Spectral analysis for sampling image-based rendering data. IEEE Transactions on Circuits Systems for Video Technology13(11), 1038–1050 (2003)

    2003

  32. [33]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)

    2018

  33. [34]

    In: IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Zhang, Z., Liu, Y., Dai, Q.: Light field from micro-baseline image pair. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3800– 3809 (2015)

    2015

  34. [35]

    ACM Transactions on Graphics42(5), 1–16 (2023)

    Zheng, C., Huo, Y., Mo, S., Zhong, Z., Wu, Z., Hua, W., Wang, R., Bao, H.: Nelt: Object-oriented neural light transfer. ACM Transactions on Graphics42(5), 1–16 (2023)

    2023

  35. [36]

    ACM Transactions on Graphics37(4), 1–12 (2018)

    Zhou, T., Tucker, R., Flynn, J., Fyffe, G., Snavely, N.: Stereo magnification: learn- ing view synthesis using multiplane images. ACM Transactions on Graphics37(4), 1–12 (2018)

    2018