Year: 2025
Type: article
Abstract: Human mesh recovery (HMR) holds significant utility in many applications. Studying HMR involving various types of sensors is necessary, as it enables the acquisition of human meshes in diverse scenes. Unlike HMR based on RGB images, HMR based on LiDAR has received considerably less attention in previous works. The major challenge in estimating human poses and meshes from sparse point clouds lies in the sparsity, noise, and incompletion of LiDAR point clouds. To address these challenges, we propose a LiDAR-based 3D human mesh recovery algorithm, called LiDAR-HMR. This algorithm involves estimating a sparse representation of a human (3D human pose) and gradually reconstructing the body mesh. To better leverage the 3D structural information of point clouds, we propose a point-cloud-to-SMPL pipeline that uses the original point cloud features to guide the reconstruction. The experimental results on four publicly available datasets demonstrate the effectiveness of LiDAR-HMR. The codes are available athttps://github.com/soullessrobot/LiDAR-HMR.
Source: IEEE Transactions on Multimedia
Authors: Bohao Fan, Wenzhao Zheng, Jianjiang Feng, Jie Zhou
Institution: Tsinghua University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1195549577, G2438210826
OpenAlex ID: W4412567283