SCALABLE POINT CLOUD COMPRESSION WITH TRANSFORM, AND CORRESPONDING DECOMPRESSION

Innovations in scalable compression and decompression of point cloud data are described. For example, after an encoder uses a transform such as a region-adaptive hierarchical transform (“RAHT”) on attributes of occupied points in point cloud data, the encoder separates transform coefficients into partitions. The partitions can be associated with different regions of a point cloud frame (spatial location scalability), different spatial resolutions of point cloud data (spatial resolution scalability), different reconstruction quality levels (SNR scalability), different point cloud frames organized in temporal layers (temporal resolution scalability), or different combinations of the preceding types of partitions. For decoding, a decoder can select all of the partitions or a subset of the partitions. The decoder decodes encoded data for the selected partitions, applying an inverse transform such as an inverse RAHT to transform coefficients for attributes of occupied points in point cloud data.