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]
日期:2023-01-01
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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