Rate-Distortion-Guided Learning Approach with Cross-Projection Information for V-PCC Fast CU Decision

In video-based point cloud compression (V-PCC), the 3D dynamic point cloud sequence is projected into 2D sequences for compression by utilizing the mature 2D video encoder. It is noted the encoding of attribute sequence is extremely time-consuming, and the applicable fast algorithms are still lacking because of the uniqueness of video content and coding structure in V-PCC. This paper proposes a novel rate-distortion-guided fast attribute coding unit (CU) partitioning approach with cross-projection information in V-PCC all-intra (AI) coding. By analyzing the guidance effectiveness of cross-projection information for attribute CU partition, we first propose to combine the occupancy, geometry and attribute features for CU division determination. Afterward, considering that different CUs have different rate-distortion costs and the influences on coding performances by inaccurate different CU predictions are also dissimilar, we devise a rate-distortion-guided learning approach to reduce the coding loss generated by the mispredictions of CU partition. Moreover, we carefully design an overall decision framework for CU partition in V-PCC AI coding structure. Experimental results prove the advantages of our approach, where the coding time is saved by 62.41%, and the End-to-End BD-TotalRate loss only is 0.27%. To the best of our knowledge, the proposed fast attribute CU decision approach achieves the state-of-the-art performance in V-PCC AI coding.