Unsupervised Non-Rigid Human Point Cloud Registration Based on Deformation Field Fusion

Abstract

Human point cloud registration is a critical problem in the fields of computer vision and computer graphics applications. Currently, due to the presence of joint hinges and limb occlusions in human point clouds, point cloud alignment is challenging. To address these two limits, this paper proposes an unsupervised non-rigid human point cloud registration method based on deformation field fusion. The method mainly consists of the deep dynamic link deformation field estimation module and the probabilistic alignment deformation field estimation module. The deep dynamic link deformation field estimation module uses a time series network to convert non-rigid deformation into multiple rigid deformations. Then, feature extraction is performed to estimate the deformation field based on the rigid deformations. The probabilistic alignment deformation field estimation module builds on a Gaussian mixture model and adds local and global constraint conditions for deformation field estimation. Finally, the two deformation fields are fused into the total deformed field by aligning them, which enhances the sensitivity to both global and local feature information. The experimental results on public datasets and real private datasets demonstrate that the proposed method has higher accuracy and better robustness under joint hinges and limb adhesion conditions.