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 a 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 PCGCv2 baseline, TAFA-GSGC demonstrates improved RD performance, achieving average BD-rate reductions of 4.99% and 5.92% in terms of D1-PSNR and D2-PSNR, respectively.
@article{arxiv.2604.28045,
title = {TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement},
author = {Xiumei Li and Alexander Kopte and André Kaup},
journal= {arXiv preprint arXiv:2604.28045},
year = {2026}
}
Comments
Accepted at IEEE International Conference on Image Processing (ICIP) 2026