用可学习的球面分区替代球谐函数，提升3D高斯点云的渲染真实感。
Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere
- 将球面划分为可学习区域，实现平滑方向性外观建模
- 在反射效果上超越现有方法，合成与真实数据集均达顶尖表现
- 适合需要高质量视点相关渲染的研究者与工业应用
基于辐射场的方法（如3D高斯泼溅）在新视角合成中表现出强大能力，但其外观建模常依赖球谐函数（SH），存在高频信号处理差、吉布斯振荡伪影及无法捕捉镜面反射等根本局限。尽管球面高斯等替代方案有所改进，却显著增加优化复杂度。本文提出球面沃罗诺伊（Spherical Voronoi, SV）作为3D高斯泼溅中统一的外观表示框架。SV将方向域划分为可学习的区域，边界平滑，为视点相关效应提供直观且稳定的参数化方式。对于漫反射外观，SV实现竞争力结果，同时比现有方案更易优化；对于镜面反射——传统SH失效之处——我们利用SV作为可学习的反射探针，以反射方向为输入，遵循经典图形学原理。该方法在合成与真实数据集上均达到当前最优性能，证明了SV是显式3D表示中外观建模的一种原理性强、高效且通用的解决方案。
原文摘要 · Abstract (English)
Radiance field methods (e.g. 3D Gaussian Splatting) have emerged as a powerful paradigm for novel view synthesis, yet their appearance modeling often relies on Spherical Harmonics (SH), which impose fundamental limitations. SH struggle with high-frequency signals, exhibit Gibbs ringing artifacts, and fail to capture specular reflections - a key component of realistic rendering. Although alternatives like spherical Gaussians offer improvements, they add significant optimization complexity. We propose Spherical Voronoi (SV) as a unified framework for appearance representation in 3D Gaussian Splatting. SV partitions the directional domain into learnable regions with smooth boundaries, providing an intuitive and stable parameterization for view-dependent effects. For diffuse appearance, SV achieves competitive results while keeping optimization simpler than existing alternatives. For reflections - where SH fail - we leverage SV as learnable reflection probes, taking reflected directions as input following principles from classical graphics. This formulation attains state-of-the-art results on synthetic and real-world datasets, demonstrating that SV offers a principled, efficient, and general solution for appearance modeling in explicit 3D representations. Project page: https://sphericalvoronoi.github.io/
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