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.

翻译：三维高斯泼溅的剪枝对降低其复杂度以实现高效存储、传输及下游处理至关重要。然而，现有剪枝策略大多依赖于相机参数、渲染图像或视角相关度量。这种依赖性在新兴的相机无关交换场景（如以点云表示形式直接共享泼溅数据，例如.ply）中成为阻碍。本文提出一种完全基于属性衍生邻域描述符的相机无关一次性后训练剪枝方法。作为核心贡献，我们引入混合描述符框架，直接从泼溅表征中捕获结构与外观一致性。基于这些描述符，我们将剪枝形式化为统计证据估计问题，并提出贝塔证据模型，通过概率置信度量化泼溅可靠性。在国际标准化组织/国际电工委员会动态图像专家组通用测试条件（ISO/IEC MPEG CTC）定义的标准测试序列上进行实验，结果表明本方法在实现显著剪枝的同时保持重建质量，为现有依赖相机的剪枝策略提供了实用且通用的替代方案。