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arXiv · 2602.00186

SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representation

Tingyu Fan·Ran Gong·Yueyu Hu·Yao Wang
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Abstract

This paper presents SurfelSoup, an end-to-end learned surface-based framework for point cloud geometry compression, with surface-structured primitives for representation. It proposes a probabilistic surface representation, pSurfel, which models local point occupancies using a bounded generalized Gaussian distribution. In addition, the pSurfels are organized into an octree-like hierarchy, pSurfelTree, with a Tree Decision module that adaptively terminates the tree subdivision for rate-distortion optimal Surfel granularity selection. 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 consistent gain on geometry compression over voxel-based baselines and MPEG standard G-PCC-GesTM-TriSoup, while providing visually superior reconstructions with smooth and coherent surface structures.

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Tingyu Fan, Ran Gong, Yueyu Hu, Yao Wang. 2026-01-30. SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representation. https://arxiv.org/abs/2602.00186

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