Advanced 3D Motion Prediction for Video Based Point Cloud Attributes Compression

Abstract

Point cloud media representation format has provided various opportunities for extended reality applications and had become widely used in volumetric content capturing scenarios. At the same time ambiguous storage format representations and network throughput are key problems for wide adoption of this media format. Compression algorithms in corresponding standard activities are aimed to solve this problem. MPEG-I standard has an aim of creating the point cloud compression methodology relying on existing video coding hardware implementations. In scope of the state-of-the-art video-based dynamic point cloud (DPC) compression method, similar 3D patches may be projected in totally different 2D positions in different frames. In this way, the motion vector predictors especially those in the patch boundary may be very inaccurate which may lead to significant bitrate increase. In this paper, we propose to use the reconstructed geometry information to help predict the motion vector more accurately and improve the coding efficiency of the attribute video. First, we propose to use the motion vector of the co-located blocks in the geometry frame as a merge candidate of the current block in the attribute frame. Second, we perform a motion estimation between the current reconstructed point cloud with only the geometry information and the reference point cloud to find the corresponding block. The motion information derived is used as motion vector predictor of the current block in the attribute frame. As far as we can see, this is the first work using the geometry information to compress the attribute in the DPC compression scenario. Significant compression efficiency is achieved with this new 3D point cloud geometry derived motion prediction scheme when compared with the state-of-the-art DPC compression method.