Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding
This work addresses compression efficiency for dynamic point clouds, which is crucial for applications like virtual reality and 3D video, but it is incremental as it builds on an existing static compression method.
The paper tackles dynamic point cloud geometry compression by extending a multiscale sparse representation framework with inter conditional coding, achieving a 78% lossy BD-rate gain over V-PCC and 45% lossless bitrate reduction compared to G-PCC.
This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding. To this end, the reconstruction of the preceding Point Cloud Geometry (PCG) frame is progressively downscaled to generate multiscale temporal priors which are then scale-wise transferred and integrated with lower-scale spatial priors from the same frame to form the contextual information to improve occupancy probability approximation when processing the current PCG frame from one scale to another. Following the Common Test Conditions (CTC) defined in the standardization committee, the proposed method presents State-Of-The-Art (SOTA) compression performance, yielding 78% lossy BD-Rate gain to the latest standard-compliant V-PCC and 45% lossless bitrate reduction to the latest G-PCC. Even for recently-emerged learning-based solutions, our method still shows significant performance gains.