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arxiv: 2606.00450 · v1 · pith:7YQ3NM2Gnew · submitted 2026-05-30 · 💻 cs.CV · cs.GR

Optimizing 3D Gaussian Splatting via Point Cloud Upsampling

Adrian Ramlal , Yan Song Hu , John S. Zelek This is my paper

Pith reviewed 2026-06-28 19:20 UTC · model grok-4.3

classification 💻 cs.CV cs.GR
keywords 3D Gaussian Splattingpoint cloud upsamplingStructure-from-Motiondepth-guided liftingscene reconstructionMip-NeRF360Replica datasetinitialization
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The pith

Point cloud upsampling strategies raise 3D Gaussian Splatting reconstruction quality on Mip-NeRF360 and Replica datasets.

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

The paper evaluates multiple point cloud upsampling methods to strengthen the initial seed points used in 3D Gaussian Splatting. Methods tested include linear and triangular interpolation, spline-based surface reconstruction, moving least squares fitting, Voronoi-based generation, plus a new depth-guided lifting technique that draws on depth maps for geometric consistency with SfM output. Experiments across the Mip-NeRF360 and Replica datasets show measurable quality gains that differ by scene type. Surface-oriented methods work best on organic, detailed scenes while simpler interpolation suffices for piecewise-smooth geometries, and the depth-guided method supplies useful points in texture-less regions. The results yield preliminary selection guidelines tied to scene characteristics and available compute.

Core claim

The paper claims that upsampling SfM-derived seed points via linear interpolation, triangular interpolation, spline-based surface reconstruction, moving least squares surface fitting, Voronoi-based generation, and depth-guided point lifting produces higher-quality 3D Gaussian Splatting reconstructions, with surface reconstruction methods favored for organic detailed scenes, simpler interpolation favored for piecewise-smooth geometries, and depth-guided lifting effective for adding geometry-aware points in texture-less regions.

What carries the argument

Depth-guided point lifting that uses depth maps to generate additional points while preserving consistency with the original Structure-from-Motion reconstruction.

If this is right

  • Surface reconstruction methods perform better with organic, detailed scenes.
  • Simpler interpolation approaches are more suited for scenes dominated by piecewise-smooth geometries.
  • The depth-guided approach adds geometry-aware points across the entire scene, including texture-less regions.
  • The findings supply preliminary practical guidelines for choosing an upsampling method based on scene characteristics and computational constraints.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same upsampling choices could be tested on other point-based novel-view-synthesis pipelines to check whether the scene-type pattern holds.
  • Depth-guided lifting might reduce the density needed from the initial SfM stage, lowering capture time or hardware requirements.
  • The guidelines could be turned into an automatic selector that inspects scene statistics before choosing the upsampling strategy.

Load-bearing premise

That measured quality gains come from the upsampling itself and that the added points stay geometrically consistent with the original SfM points rather than from other unstated pipeline changes.

What would settle it

A side-by-side run of 3DGS with and without each upsampling method on the same SfM points, or a direct check that upsampled points deviate from the original SfM geometry by more than a small threshold.

Figures

Figures reproduced from arXiv: 2606.00450 by Adrian Ramlal, John S. Zelek, Yan Song Hu.

Figure 1
Figure 1. Figure 1: The 16x upscaled point clouds of garden from Mip-NeRF360 [15]. achieves a PSNR of 31.874 at 4x upsampling, represent￾ing a notable improvement over the baseline PSNR of 31.559. Interestingly, the simpler Linear and Triangle interpolation methods also show promise, particularly at lower upsampling ratios. For instance, the Linear method at 8x upsampling achieves a PSNR of 32.660, an improvement of 0.369 dB,… view at source ↗
Figure 2
Figure 2. Figure 2: The 32x upscaled point clouds of office3 from Replica [16]. 4.2.1 Guidelines for Method Selection Surface reconstruction methods (MLS and Voronoi) con￾sistently outperform simple interpolation in all scenes. Again, as established with Mip-NeRF360 [15], the Voronoi method improves initialization for scenes con￾taining planar, low-texture surfaces. MLS maintains higher PSNR likely due to its ability to handl… view at source ↗
read the original abstract

3D Gaussian Splatting (3DGS) is a technique for creating and rendering 3D scenes, however its performance depends heavily on the quality of initial seed points. To improve 3DGS initialization, this study presents and evaluates several point cloud upsampling approaches: linear interpolation, triangular interpolation, spline-based surface reconstruction, moving least squares surface fitting, and Voronoi-based point generation. Additionally, this research introduces a depth-guided point lifting method that leverages depth maps to maintain geometric consistency with Structure-from-Motion (SfM) reconstructions. Through extensive experiments on the Mip-NeRF360 and Replica datasets, the proposed methods demonstrate improvements in reconstruction quality across diverse scene types. Results indicate that different upsampling strategies excel in different scenarios: surface reconstruction methods perform better with organic, detailed scenes, while simpler interpolation approaches are more suited for scenes dominated by piecewise-smooth geometries. In comparison, the depth-guided approach shows promise for adding geometry-aware points across the entire scene, importantly in texture-less regions. These findings, which provide preliminary practical guidelines for selecting appropriate upsampling methods based on scene characteristics and computational constraints, advances the understanding of how point cloud initialization affects 3DGS quality.

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

2 major / 1 minor

Summary. The manuscript proposes and evaluates multiple point cloud upsampling strategies (linear/triangular interpolation, spline-based reconstruction, moving least squares, Voronoi-based generation, and a new depth-guided lifting method) to improve the initial seed points for 3D Gaussian Splatting. Experiments on the Mip-NeRF360 and Replica datasets are reported to yield reconstruction quality gains, with the claim that different strategies are preferable for different scene types (surface methods for organic scenes, interpolation for piecewise-smooth geometries) and that depth-guided lifting helps in textureless regions.

Significance. If the attribution of gains to the upsampling methods can be isolated from other pipeline variables, the work would supply practical, scene-dependent heuristics for 3DGS initialization that could be adopted by practitioners working with SfM-derived point clouds.

major comments (2)
  1. [Abstract / Experiments] Abstract and Experiments: the central claim that the listed upsampling strategies produce the observed quality improvements requires that all other 3DGS pipeline elements (densification schedule, learning rates, Gaussian initialization code, optimization hyperparameters) remain identical once the point cloud is replaced. No description is given that such controls were enforced, so attribution to upsampling cannot be verified.
  2. [Abstract] Abstract: the assertion of 'improvements in reconstruction quality across diverse scene types' is presented without any quantitative metrics, baseline numbers, error bars, or statistical tests, rendering the magnitude and reliability of the claimed gains impossible to assess.
minor comments (1)
  1. [Abstract] Abstract, final sentence: subject-verb agreement error ('findings ... advances') should read 'advance'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the presentation of our experimental methodology and results. We address each major comment below and will make the necessary revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments: the central claim that the listed upsampling strategies produce the observed quality improvements requires that all other 3DGS pipeline elements (densification schedule, learning rates, Gaussian initialization code, optimization hyperparameters) remain identical once the point cloud is replaced. No description is given that such controls were enforced, so attribution to upsampling cannot be verified.

    Authors: We agree that explicit confirmation of controlled variables is essential for attributing gains to the upsampling methods. In our experiments, the 3DGS implementation, densification schedule, learning rates, Gaussian initialization code, and all optimization hyperparameters were held fixed, with only the input point cloud varying. We acknowledge that this control was not described in the manuscript. We will revise the Experiments section to explicitly document these controls and confirm that the same codebase and settings were used throughout. revision: yes

  2. Referee: [Abstract] Abstract: the assertion of 'improvements in reconstruction quality across diverse scene types' is presented without any quantitative metrics, baseline numbers, error bars, or statistical tests, rendering the magnitude and reliability of the claimed gains impossible to assess.

    Authors: We agree that the abstract should provide quantitative support for the claims. While the full manuscript reports PSNR, SSIM, and LPIPS results on Mip-NeRF360 and Replica with baseline comparisons, the abstract summarizes these without numbers. We will revise the abstract to include key quantitative metrics (e.g., average improvements) and reference the variability across scene types, while directing readers to the detailed tables and any error bars or standard deviations in the Experiments section. revision: yes

Circularity Check

0 steps flagged

Empirical evaluation paper with no derivation chain

full rationale

The manuscript proposes and tests several point-cloud upsampling heuristics (linear/triangular interpolation, spline, MLS, Voronoi, depth-guided lifting) followed by standard 3DGS training. No equations, fitted parameters, or first-principles derivations are presented; all claims rest on direct experimental comparison against baselines on Mip-NeRF360 and Replica. No self-citation is invoked to justify uniqueness or to close a logical loop. The attribution of quality gains to the upsampling step is an empirical question that can be falsified by re-running the identical 3DGS pipeline, but that is a methodological concern rather than circularity in any derivation.

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; ledger left empty.

pith-pipeline@v0.9.1-grok · 5744 in / 954 out tokens · 21707 ms · 2026-06-28T19:20:19.301879+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

18 extracted references · 7 canonical work pages · 1 internal anchor

  1. [1]

    3d gaussian splatting for real-time radiance field rendering,

    B. Kerbl, G. Kopanas, T. Leimk ¨uhler, and G. Dret- takis, “3d gaussian splatting for real-time radiance field rendering, ”ACM Transactions on Graphics, vol. 42, no. 4, July 2023. [Online]. Available: https:// repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

    2023

  2. [2]

    Poison-splat: Computation cost attack on 3d gaussian splatting,

    J. Lu, Y. Zhang, Q. Shen, X. Wang, and S. Yan, “Poison-splat: Computation cost attack on 3d gaussian splatting, ” 2024. [Online]. Available: https://arxiv.org/abs/2410.08190

    work page arXiv 2024

  3. [3]

    Structure- from-motion revisited,

    J. L. Sch ¨onberger and J.-M. Frahm, “Structure- from-motion revisited, ” inConference on Computer Vision and Pattern Recognition (CVPR), 2016

    2016

  4. [4]

    Lidar-3dgs: Lidar reinforced 3d gaussian splatting for multimodal radiance field rendering,

    H. Lim, H. Chang, J. B. Choi, and C. M. Yeum, “Lidar-3dgs: Lidar reinforced 3d gaussian splatting for multimodal radiance field rendering, ”

  5. [5]

    Available: https://arxiv.org/abs/ 2409.16296

    [Online]. Available: https://arxiv.org/abs/ 2409.16296

    work page arXiv

  6. [6]

    Rgbd gs-icp slam,

    S. Ha, J. Yeon, and H. Yu, “Rgbd gs-icp slam, ”

  7. [7]

    Available: https://arxiv.org/abs/ 2403.12550

    [Online]. Available: https://arxiv.org/abs/ 2403.12550

    work page arXiv

  8. [8]

    Instantsplat: Unbounded sparse-view pose-free gaussian splatting in 40 sec- onds,

    Z. Fan, W. Cong, K. Wen, K. Wang, J. Zhang, X. Ding, D. Xu, B. Ivanovic, M. Pavone, G. Pavlakos, Z. Wang, and Y. Wang, “Instantsplat: Unbounded sparse-view pose-free gaussian splatting in 40 sec- onds, ” 2024

    2024

  9. [9]

    Dust3r: Geometric 3d vision made easy,

    S. Wang, V. Leroy, Y. Cabon, B. Chidlovskii, and J. Revaud, “Dust3r: Geometric 3d vision made easy, ” 2024. [Online]. Available: https: //arxiv.org/abs/2312.14132

    work page arXiv 2024

  10. [10]

    Point cloud densification for 3d gaussian splatting from sparse input views,

    K.-C. Chan, J. Xiao, H. L. Goshu, and K.-M. Lam, “Point cloud densification for 3d gaussian splatting from sparse input views, ” inProceedings of the 32nd ACM International Conference on Multimedia, ser. MM ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 896–904. [Online]. Available: https://doi.org/10.1145/3664647.3681454

    work page doi:10.1145/3664647.3681454 2024

  11. [11]

    Dense 3D Gaussian Splatting Initialization for Sparse Image Data,

    S. Seibt, T. V. S.-L. Chang, B. von Rymon Lipinski, and M. E. Latoschik, “Dense 3D Gaussian Splatting Initialization for Sparse Image Data, ” inEurograph- ics 2024 - Posters, L. Liu and M. Averkiou, Eds. The Eurographics Association, 2024

    2024

  12. [12]

    To- wards real-time gaussian splatting: Accelerating 3dgs through photometric slam,

    Y. S. Hu, D. Mao, Y. Chen, and J. Zelek, “To- wards real-time gaussian splatting: Accelerating 3dgs through photometric slam, ” 2024. [Online]. Available: https://arxiv.org/abs/2408.03825

    work page arXiv 2024

  13. [13]

    Piegl and W

    L. Piegl and W. Tiller,The NURBS book (2nd ed.), ser. Monographs in Visual Communication. Berlin, Heidelberg: Springer-Verlag, 1997, p. 100–110

    1997

  14. [14]

    Approximate moving least- squares approximation: A fast and accurate multivariate approximation method,

    G. E. Fasshauer, “Approximate moving least- squares approximation: A fast and accurate multivariate approximation method, ” inCurve and Surface Fitting, A. Cohen, J.-L. Merrien, and L. L. Schumaker, Eds. Saint-Malo 2002: Nashboro Press, 2003, pp. 139–148. [Online]. Available: https: //api.semanticscholar.org/CorpusID:10669315

    2002

  15. [15]

    Surface reconstruction from points,

    W. Y. Chang, “Surface reconstruction from points, ” UC San Diego, Department of Computer Science & Engineering, Technical Report CS2008-0922, may 2008, peer-Reviewed. [Online]. Available: https://escholarship.org/uc/item/9xv8x6n6

    2008

  16. [16]

    Point cloud densification for 3d gaussian splatting from sparse input views,

    K.-C. Chan, J. Xiao, H. L. Goshu, and K. man Lam, “Point cloud densification for 3d gaussian splatting from sparse input views, ” inACM Multimedia 2024, 2024. [Online]. Available: https: //openreview.net/forum?id=vW5070FoXi

    2024

  17. [17]

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

    J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srini- vasan, and P. Hedman, “Mip-nerf 360: Unbounded anti-aliased neural radiance fields, ”CVPR, 2022

    2022

  18. [18]

    The Replica Dataset: A Digital Replica of Indoor Spaces

    J. Straub, T. Whelan, L. Ma, Y. Chen, E. Wij- mans, S. Green, J. J. Engel, R. Mur-Artal, C. Ren, S. Verma, A. Clarkson, M. Yan, B. Budge, Y. Yan, X. Pan, J. Yon, Y. Zou, K. Leon, N. Carter, J. Briales, T. Gillingham, E. Mueggler, L. Pesqueira, M. Savva, D. Batra, H. M. Strasdat, R. D. Nardi, M. Goe- sele, S. Lovegrove, and R. Newcombe, “The Replica datase...

    work page internal anchor Pith review Pith/arXiv arXiv 1906