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This work extends the Multiscale Sparse Representation (MSR) frameworkdeveloped for static Point Cloud Geometry Compression (PCGC) to support thedynamic PCGC through the use of multiscale inter conditional coding. To thisend, the reconstruction of the preceding Point Cloud Geometry (PCG) frame isprogressively downscaled to generate multiscale temporal priors which are thenscale-wise transferred and integrated with lower-scale spatial priors from thesame frame to form the contextual information to improve occupancy probabilityapproximation when processing the current PCG frame from one scale to another.Following the Common Test Conditions (CTC) defined in the standardizationcommittee, the proposed method presents State-Of-The-Art (SOTA) compressionperformance, yielding 78% lossy BD-Rate gain to the latest standard-compliantV-PCC and 45% lossless bitrate reduction to the latest G-PCC. Even forrecently-emerged learning-based solutions, our method still shows significantperformance gains.

Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding