不依赖相机参数的3D高斯点云压缩方法，提升通用性与效率。
Camera-Agnostic Pruning of 3D Gaussian Splats via Descriptor-Based Beta Evidence
- 基于点云属性构建结构与外观一致性描述符
- 用贝塔分布建模每点可靠性，实现一次性剪枝
- 在ISO/IEC MPEG CTC测试中兼顾压缩率与重建质量
3D高斯点云的剪枝对降低复杂度、实现高效存储、传输和下游处理至关重要。然而，现有剪枝策略大多依赖相机参数、渲染图像或视图相关度量，这在新兴的无相机依赖共享场景（如直接以.ply格式交换点云）中成为障碍。本文提出一种无需相机信息、一次完成、训练后适用的3D高斯点云剪枝方法，仅依赖属性导出的邻域描述符。主要贡献为引入混合描述符框架，直接从点云表示中捕捉结构与外观一致性；在此基础上，将剪枝建模为统计证据估计问题，提出贝塔证据模型，通过概率置信度量化每点的可靠性。在ISO/IEC MPEG公共测试条件（CTC）定义的标准测试序列上进行实验，结果表明该方法在显著压缩的同时保持了良好的重建质量，为现有依赖相机的剪枝策略提供了一种实用且可推广的替代方案。
原文摘要 · Abstract (English)
The pruning of 3D Gaussian splats is essential for reducing their complexity to enable efficient storage, transmission, and downstream processing. However, most of the existing pruning strategies depend on camera parameters, rendered images, or view-dependent measures. This dependency becomes a hindrance in emerging camera-agnostic exchange settings, where splats are shared directly as point-based representations (e.g., .ply). In this paper, we propose a camera-agnostic, one-shot, post-training pruning method for 3D Gaussian splats that relies solely on attribute-derived neighbourhood descriptors. As our primary contribution, we introduce a hybrid descriptor framework that captures structural and appearance consistency directly from the splat representation. Building on these descriptors, we formulate pruning as a statistical evidence estimation problem and introduce a Beta evidence model that quantifies per-splat reliability through a probabilistic confidence score. Experiments conducted on standardized test sequences defined by the ISO/IEC MPEG Common Test Conditions (CTC) demonstrate that our approach achieves substantial pruning while preserving reconstruction quality, establishing a practical and generalizable alternative to existing camera-dependent pruning strategies.
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