Rate-Adaptive Context Evolution for Progressive 3D Gaussian Coding

Progressive 3D Gaussian Splatting (3DGS) compression decodes a hierarchy of refinement levels from a single bitstream. However, existing progressive codecs largely rely on entropy contexts whose content or spatial granularity does not adapt to the level-dependent distribution of active Gaussians, limiting probability estimation. We introduce Rate-Adaptive Context Evolution (RACE), which treats the entropy context as a causal state that evolves with decoded content. RACE first splats previously decoded Gaussian attributes onto directional hint maps to update a shared triplane prior. It then selects a directional granularity tuple using encoder-side rate estimates and transmits the selected indices, allowing the decoder to reconstruct the same context without repeating the search. Finally, cross-level attention uses the preceding-level anchor state to condition the aggregation of the current-level spatial neighborhood. Built on the progressive framework of PCGS, RACE produces multiple operating points from a single bitstream. Across 19 scenes from four benchmarks, RACE achieves the lowest aggregate compression rank among the compared methods with complete four-dataset coverage, while keeping decoding time close to PCGS. These results demonstrate the benefit of adapting context content, spatial scale, and cross-level interaction for progressive 3DGS entropy modeling.