Nonrigid Registration-Based Progressive Motion Compensation for Point Cloud Geometry Compression

点云 运动补偿 计算机科学 计算机视觉 四分之一像素运动 运动场 运动估计 人工智能 块匹配算法 点集注册 刚性变换 算法 点(几何) 几何学 数学 视频处理 视频跟踪
作者
Yong Shao,Ge Li,Qi Zhang,Wei Gao,Shan Liu
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-14
标识
DOI:10.1109/tgrs.2023.3321289
摘要

There is a critical requirement for efficiently compressing point cloud geometries representing three-dimensional (3D) moving objects in various applications. The Moving Picture Experts Group 3D Graphics coding group (MPEG 3DG) set up an inter-exploration model for geometry-based point cloud compression (G-PCC interEM). However, the block-matching motion compensation scheme with a translational motion model has limited ability to handle dense point clouds with complex local motions. To overcome this problem, we propose a progressive non-rigid motion compensation framework for point cloud geometry compression, where the point cloud registration technique is introduced and tailored with our designed rate-distortion cost. In the coarse-grained stage, a point cloud is represented as deformable point patches, and the patch-wise non-rigid motion estimation task is formulated as a registration-based optimization problem that can be efficiently solved by the majorization-minimization method. In the fine-grained stage, we propose a block-based motion refinement to enhance the estimated motion field in the local region, followed by a multi-hypothesis motion compensation scheme enabling smooth reference reconstruction with patch-wise deformation and block-wise refined motions. Experiments demonstrate our proposed scheme outperforms several competitive platforms in terms of both coding performance and compensation quality. Compared with G-PCC interEM, our proposed framework achieves significant bitrate savings, i.e., 4.71% (32 frames) and 4.22% (200 frames), for point cloud lossless geometry compression.

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