Abstract: In video-based point cloud compression(V-PCC),a 3D point cloud is divided into hundreds of patches and then mapped onto a 2D grid,generating a texture video that captures texture information and a geometry video that captures geometry information.Meanwhile,an occupancy map video is also generated to record whether each pixel in the former two videos corresponds to a point in the reconstructed point cloud.Therefore,the quality of the occupancy map video is directly linked to the quality of the reconstructed point cloud.To save bit cost,the occupancy map video is down-sampled at the encoder and up-sampled with a simplistic method at the decoder.This paper aims to use a deep learning-based up-sampling method to replace the simple up-sampling method in the original V-PCC to improve the quality of the up-sampled occupancy map videos as well as that of the reconstructed point cloud.A weighted distortion loss function in the network training process is introduced to remove the normal points as few as possible while removing the noisy points as many as possible when reconstructing a point cloud.Experimental results show that the proposed method can significantly improve the subjective and objective performances of the V-PCC.

Key words: Point cloud compression, Video-based point cloud compression standard, Occupancy map video, Video up-sampling, Weighted loss