弹性三角形splatting
Elastic Triangle Splatting
- Technical University of Munich（慕尼黑工业大学）
- MCML（慕尼黑计算与机器学习实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对三角形splatting的核函数选择问题，提出弹性核函数，经实验验证其在形状重建和多数新视图合成基准中优于现有核，凸显核设计的重要性。
AI中文摘要：
尽管3D高斯splatting等神经渲染方法实现了出色的视觉保真度，但传统多边形网格仍是成熟图形管线的核心。三角形splatting通过将三角形基元优化为可微splat，弥合了这一差距，生成更接近基于网格工作流的表示。这些方法的核心是核函数，其作用是柔化三角形边界以将梯度传播到顶点位置。现有三角形splatting方法对核函数的选择不一致，且对这些核优化行为的分析仅局限于用于新视图合成的非结构化三角形集合。本研究将三角形splatting视为光度优化的通用工具，通过两个互补任务比较核属性：用于形状重建的网格优化和用于新视图合成的三角形集合优化。在分析的同时，我们引入了弹性核函数，该函数在边界处具有双边梯度支持和自适应边界值，这些特性被证明对稳健优化至关重要。在孤立比较中，我们的弹性核在形状重建任务上优于现有核，且在大多数新视图合成基准测试中表现更优，证明了核设计对三角形splatting的有效性和通用性的重要性。
英文摘要：
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.
