Joint Lossless Point Cloud Geometry and Attribute Compression via Causal Multiscale Coding

Geometry and attributes in colored point clouds are correlated, yet learned lossless codecs primarily exploit this dependency from geometry to attributes, leaving decoded attribute information unused for geometry probability estimation. A seemingly natural joint variant is to share geometry–attribute features throughout the encoder and decoder; however, this naive feature sharing does not necessarily improve overall lossless compression efficiency. We present a causal multiscale framework for joint lossless geometry and attribute compression. At each scale transition, decoded coarse-scale attributes condition finer-scale occupancy prediction; the finer-scale decoded geometry then provides support and context for attribute probability modeling. A shared joint processing unit (JPU) implements this interleaved cross-modal conditioning, with parameters reused across scales and modality-specific refinements for the two entropy models. On eight static point clouds from 8iVFB and Owlii, our method reduces geometry and total bitrate on every sequence relative to the separate Unicorn geometry and attribute codecs. On average, geometry, attribute, and total bitrates decrease by 5.0%, 0.7%, and 1.0%, respectively, while using 16.8% fewer trainable parameters. Under the same hardware and test settings, our method achieves 1.74$\times$ faster encoding and 1.69$\times$ faster decoding than the combined Unicorn codecs. Compared with the conventional codec G-PCC, the proposed method reduces the average total bitrate by 12.12%.