Title: SurfelSoup: Probabilistic G-SurfelTree for Learned Point Cloud Geometry Compression
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Point cloud compression; Surfel representation; Probabilistic surface modeling; Generalized Gaussian distribution

Abstract: This paper presents SurfelSoup, the first end-to-end learned surface-based framework for dense point cloud geometry compression. It proposes a probabilistic and differentiable surface representation, G-Surfel, which models local point occupancies using a bounded generalized Gaussian distribution. We further introduce G-SurfelTree, an octree-like hierarchy, where a decision module adaptively terminates the tree subdivision  to achieve rate-distortion optimal G-Surfel granularity selection.
%for optimal G-Surfel granularity selection under a unified rate–distortion trade-off.  
This formulation avoids redundant point-wise compression in smooth regions and produces compact yet smooth surface reconstructions. Experimental results under the MPEG common test condition show that SurfelSoup achieves consistent gain on dense geometry compression over voxel-based baselines and MPEG standard G-PCC-GesTM-TriSoup, while providing visually superior reconstructions with smooth and coherent surface structures.

TL;DR: This paper presents SurfelSoup, the first end-to-end learned surface-based framework for dense point cloud geometry compression.