用概率表面元压缩点云，重建更平滑且更高效。
SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representation
- 用有界广义高斯分布建模局部点占据，形成概率表面元
- 通过自适应终止的树结构选择最优细节粒度，提升压缩效率
- 在标准测试中优于传统体素方法，视觉效果更连贯
本文提出 SurfelSoup，一种端到端的基于表面的点云几何压缩框架，采用结构化表面原语进行表示。提出概率表面表示 pSurfel，利用有界广义高斯分布建模局部点占据情况。同时，将 pSurfels 组织成类似八叉树的层次结构 pSurfelTree，配备树决策模块，可自适应终止树的细分，实现率失真最优的表面粒度选择。该方法避免了光滑区域的冗余点级压缩，生成紧凑且平滑的表面重构结果。在 MPEG 公共测试条件下，实验表明其几何压缩性能持续优于体素基基线方法及 MPEG 标准 G-PCC-GesTM-TriSoup，同时提供视觉上更优的重建效果，表面结构更平滑、连贯。
原文摘要 · Abstract (English)
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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