ProGS: Towards Progressive Coding for 3D Gaussian Splatting

arXiv:2603.09703v2 Announce Type: replace Abstract: Progressive transmission of 3D Gaussian Splatting (3DGS) requires each completed transmission stage to be decodable from received data and directly renderable. This work presents ProGS, a progressive codec that organizes anchor-based 3DGS as parent-closed octree prefixes. ProGS combines parent-causal entropy coding, level-balanced anchor growth, bounded multi-prefix training, and lightweight parent-anchor refinement to improve early-prefix quality without altering the complete-model rendering path. One fixed-lambda training run yields five deployable rate--quality points from a single bitstream. Experiments on 17 scenes from three datasets evaluate rate--distortion performance, rendering speed, and transmission efficiency against progressive and single-rate baselines. On one representative scene per dataset, ProGS reaches a common quality target with 30.7 sim 60.7% fewer bytes than HAC-Rand and 22.6 sim 53.4% fewer bytes than HAC++-Rand. Across the three dataset averages, ProGS-LR uses 4.7 sim 6.1% fewer bytes than HAC-high while improving SSIM by 0.002 sim 0.041 and reducing LPIPS by 4.8 sim 51.2%. The parent-closed syntax makes every prefix causally decodable and directly renderable without future topology. ProGS-HR also yields higher endpoint SSIM and lower LPIPS than PCGS across all three dataset averages, and all five prefixes render in real time. Code is available at https://github.com/ZhiyeTang/ProGS-Official

Source: arXiv cs.CV | 2026-09-01

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