Lossless Dynamic Point Cloud Geometry Compression via Rate-Distortion Optimized Motion Estimation

Dynamic point clouds are valid representations of three-dimensional moving entities in diverse application scenarios. The comprehensive redundancy in uncompressed point clouds necessitates efficient compression methods. Motion estimation (ME) plays a crucial role in eliminating the temporal redundancy of point cloud sequences. However, existing ME methods suffer from inaccurate geometry compensation distortion measures and imbalanced rate-distortion modeling, significantly impacting the coding performance. To address these challenges, we propose a rate-distortion (R-D) optimized ME scheme for dynamic point cloud geometry compression. First, a point cloud is hierarchically decomposed into a set of macroblocks and blocks via the octree, and variable block-size ME is introduced to capture complex local motions in point clouds. Second, we propose a block-matching motion search method that integrates a new geometry compensation distortion measure, combining voxel-wise Hamming distance and point-wise Euclidean distance as a joint criterion to improve the accuracy of the ME. To mitigate complexity, local motion features are extracted to efficiently accelerate the geometry distortion measure. Third, a rate-distortion model with an adaptive Lagrange multiplier is designed to facilitate better selection of inter predictors in the motion decision stage, thereby achieving efficient coding performance. Experimental results demonstrate the improvements of our proposed scheme over rival platforms in coding performance and computational efficiency. Additional results also validate the effectiveness of key modules within the proposed R-D optimized ME framework.