Elastic Triangle Splatting

2026-08-29

AI Summary

本文通过引入一种弹性核函数，优化了三角形图元的边界处理方式，从而改进了基于三角形喷射的光度优化方法，提高了形状重建和新视图合成的效果。

Abstract

While neural rendering methods such as 3D Gaussian Splatting achieve remarkable visual fidelity, traditional polygonal meshes remain the backbone of established graphics pipelines. Triangle splatting bridges this gap by optimizing triangle primitives as differentiable splats, producing representations that are closer to mesh-based workflows. Central to these methods is the kernel function that softens triangle boundaries to propagate gradients to vertex positions. Existing triangle splatting methods make inconsistent choices of kernel functions, and analysis of these kernels' optimization behavior has been limited to unstructured triangle soups for novel-view synthesis. In this work, we consider triangle splatting as a generic tool for photometric optimization, comparing kernel properties through two complementary tasks: mesh optimization for shape reconstruction and triangle soup optimization for novel-view synthesis. Along with the analysis, we introduce an elastic kernel function that features bilateral gradient support across the boundary and an adaptive boundary value, which are shown to be essential for robust optimization. Under isolated comparison, our elastic kernel outperforms existing kernels on shape reconstruction and in the majority of novel-view synthesis benchmarks, demonstrating the importance of kernel design in the effectiveness and versatility of triangle splatting.

Problem

• triangle splatting
• kernel function
• photometric optimization
• shape reconstruction
• novel-view synthesis

Innovation

• elastic kernel function
• bilateral gradient support
• adaptive boundary value
• triangle splatting
• photometric optimization