[2603.21933] Camera-Agnostic Pruning of 3D Gaussian Splats via Descriptor-Based Beta Evidence

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.21933 (cs) [Submitted on 23 Mar 2026] Title:Camera-Agnostic Pruning of 3D Gaussian Splats via Descriptor-Based Beta Evidence Authors:Peter Fasogbon, Ugurcan Budak, Patrice Rondao Alface, Hamed Rezazadegan Tavakoli View a PDF of the paper titled Camera-Agnostic Pruning of 3D Gaussian Splats via Descriptor-Based Beta Evidence, by Peter Fasogbon and Ugurcan Budak and Patrice Rondao Alface and Hamed Rezazadegan Tavakoli View PDF HTML (experimental) Abstract: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 confi...