TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement

• URL: http://arxiv.org/abs/2604.28045v1
• Date: Thu, 30 Apr 2026 15:57:59 GMT
• Title: TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement
• Abstract summary: We propose TAFA-GSGC, a scalable learned point cloud geometry that enables multi-quality decoding from a single bitstream and a single trained model.<n>Compared with the baseline PCGCv2, TAFA-GSGC attains comparable and slightly better RD performance, achieving average BD-Rate savings of -4.99% in D1 and -5.92% in D2.
• Score: 62.57701599278593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scalable compression is essential for bandwidth-adaptive transmission, yet most learned codecs are optimized for a fixed rate-distortion point, making rate adaptation costly due to re-encoding or maintaining multiple bitstreams. In this work, we propose TAFA-GSGC, a scalable learned point cloud geometry codec that enables multi-quality decoding from a single bitstream and a single trained model. TAFA-GSGC combines layered residual refinement with channel-group entropy coding, and introduces Target-Aligned Feature Aggregation module to reduce cross-layer redundancy in enhancement residuals. Our framework supports up to 9 decodable quality levels with monotonic quality improvement as more subbitstreams are received, while maintaining strong compression efficiency. Compared with the baseline PCGCv2, TAFA-GSGC attains comparable and slightly better RD performance, achieving average BD-Rate savings of -4.99% in D1 and -5.92% in D2.

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