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

2026-01-30

AI Summary

This work proposes an end-to-end learned surface primitive compression framework to address the inefficiency of point cloud geometry compression and the lack of smoothness in reconstructed surfaces. The method introduces probabilistic surface elements (pSurfels) to model local point occupancy and constructs an adaptive octree structure, termed pSurfelTree, which incorporates a Tree Decision module for rate-distortion-optimized granularity selection. By leveraging a probabilistic generalized Gaussian distribution and a hierarchical representation, the framework effectively avoids redundant encoding in smooth regions, thereby achieving both high compression efficiency and significantly improved surface smoothness and coherence in reconstruction. Under MPEG common test conditions, the proposed approach outperforms voxel-based baselines and the G-PCC-GesTM-TriSoup standard in geometric compression performance.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• point cloud compression
• geometry compression
• surface representation
• rate-distortion optimization
• smooth reconstruction

Innovation

• pSurfel
• pSurfelTree
• learned point cloud compression
• surface-based representation
• rate-distortion optimization