TAFA-GSGC is a scalable point cloud geometry compression codec using progressive residual refinement and group-wise entropy coding that achieves average BD-rate reductions of 4.99% (D1-PSNR) and 5.92% (D2-PSNR) over PCGCv2 while supporting monotonic multi-quality decoding from a single bitstream.
Specifically, the training set is constructed from dense point clouds sampled from ShapeNet [17] containing approx- imately 26,000 models
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TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement
TAFA-GSGC is a scalable point cloud geometry compression codec using progressive residual refinement and group-wise entropy coding that achieves average BD-rate reductions of 4.99% (D1-PSNR) and 5.92% (D2-PSNR) over PCGCv2 while supporting monotonic multi-quality decoding from a single bitstream.