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Metamon-GS: Enhancing representability with variance-guided densification and light encoding.

Junyan Su1, Baozhu Zhao1, Xiaohan Zhang1

  • 1Department of Future Technology, South China University of Technology, Guangzhou, 511400, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

Metamon-GS improves 3D Gaussian Splatting (3DGS) for novel view synthesis. It enhances rendering quality by addressing color accuracy and Gaussian point artifacts using variance-guided densification and multi-level hash grids.

Keywords:
3D scene reconstructionDifferentiable rendering,Novel view synthesis

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Area of Science:

  • Computer Vision
  • Computer Graphics

Background:

  • 3D Gaussian Splatting (3DGS) represents scenes using Gaussians for novel view synthesis.
  • Anchor-based 3DGS variants improved reconstruction but face challenges in rendering performance.
  • Current methods struggle with accurate color representation under varying lighting and exhibit artifacts due to inadequate densification strategies.

Purpose of the Study:

  • To enhance rendering performance and visual quality in 3D Gaussian Splatting.
  • To address limitations in color accuracy and artifact generation in existing 3DGS methods.

Main Methods:

  • Proposed Metamon-GS, incorporating a variance-guided densification strategy.
  • Introduced a multi-level hash grid to encode global lighting conditions.
  • Variance-guided densification targets Gaussians with high gradient variance for improved reconstruction.

Main Results:

  • Metamon-GS demonstrated superior novel view synthesis quality compared to baseline and other variants.
  • The multi-level hash grid enabled accurate color reproduction across different viewpoints.
  • The proposed methods effectively reduced blurriness and needle-shaped artifacts.

Conclusions:

  • Metamon-GS offers significant improvements in rendering quality for 3D novel view synthesis.
  • The combination of variance-guided densification and multi-level hash grids effectively tackles key challenges in 3DGS.
  • The method shows promise for advancing real-time rendering and scene representation technologies.