Gaussian Mesh Renderer for Lightweight Differentiable Rendering

2026-02-16

AI Summary

This work proposes Gaussian Mesh Rendering (GMR), a novel differentiable rendering framework that integrates the efficient rasterization mechanism of 3D Gaussian splatting with triangle mesh representations. Traditional mesh-based differentiable renderers suffer from high computational costs and non-smooth gradients, hindering efficient optimization under limited memory. GMR addresses these limitations by analytically generating Gaussian primitives for each triangular face, yielding a lightweight, structure-aware renderer. This approach preserves geometric fidelity while producing smoother gradients, significantly improving optimization efficiency and reconstruction quality—particularly under small-batch, low-memory conditions.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Learning with Manifolds
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

3D Gaussian Splatting (3DGS) has enabled high-fidelity virtualization with fast rendering and optimization for novel view synthesis. On the other hand, triangle mesh models still remain a popular choice for surface reconstruction but suffer from slow or heavy optimization in traditional mesh-based differentiable renderers. To address this problem, we propose a new lightweight differentiable mesh renderer leveraging the efficient rasterization process of 3DGS, named Gaussian Mesh Renderer (GMR), which tightly integrates the Gaussian and mesh representations. Each Gaussian primitive is analytically derived from the corresponding mesh triangle, preserving structural fidelity and enabling the gradient flow. Compared to the traditional mesh renderers, our method achieves smoother gradients, which especially contributes to better optimization using smaller batch sizes with limited memory. Our implementation is available in the public GitHub repository at https://github.com/huntorochi/Gaussian-Mesh-Renderer.

Problem

• differentiable rendering
• mesh optimization
• 3D reconstruction
• rendering efficiency
• gradient computation

Innovation

• Differentiable Rendering
• 3D Gaussian Splatting
• Mesh Reconstruction
• Lightweight Renderer
• Gradient Optimization