Neural Network-based Occupancy Map Joint Sampling for Video-based Point Cloud Compression

Hang Chen, Li Li, Dong Liu, Houqiang Li

Abstract: In video-based point cloud compression (V-PCC), a point cloud is projected as a texture video and a geometry video for the following compression. Besides, an occupancy map video is generated to indicate whether each pixel in the former two videos is connected to a real point in the point cloud. For bits saving purpose, the rough down-sampling before encoding and up-sampling after decoding impairs the accuracy of the map and therefore causes the quality loss of reconstructed point cloud. To tackle this problem, we propose a joint sampling network consisting of a compact resolution (CR) network and a super resolution (SR) network to replace the sampling process in V-PCC. We also modify the geometry video to make it more compatible with the new occupancy map video. Comprehensive experiments demonstrate that our sampling method for occupancy map video can bring performance improvement for point cloud. The BD-rate for geometry bits could be saved as much as 8.6 percents on D2 measurement.