{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lidar-hmr-3d-human-mesh-recovery-from-lidar","title":"LiDAR-HMR: 3D Human Mesh Recovery from LiDAR","arxiv_id":"2311.11971","date":"2023-11-20","proceeding":null,"authors":["Bohao Fan","Wenzhao Zheng","Jianjiang Feng","Jie zhou"],"abstract":"In recent years, point cloud perception tasks have been garnering increasing attention. This paper presents the first attempt to estimate 3D human body mesh from sparse LiDAR point clouds. We found that the major challenge in estimating human pose and mesh from point clouds lies in the sparsity, noise, and incompletion of LiDAR point clouds. Facing these challenges, we propose an effective sparse-to-dense reconstruction scheme to reconstruct 3D human mesh. This 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 employ a cascaded graph transformer (graphormer) to introduce point cloud features during sparse-to-dense reconstruction. Experimental results on three publicly available databases demonstrate the effectiveness of the proposed approach. Code: https://github.com/soullessrobot/LiDAR-HMR/","url_abs":"https://arxiv.org/abs/2311.11971v1","url_pdf":"https://arxiv.org/pdf/2311.11971v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lidar-hmr-3d-human-mesh-recovery-from-lidar","repo_url":"https://github.com/soullessrobot/lidar-hmr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"lidar-hmr-3d-human-mesh-recovery-from-lidar","repo_url":"https://github.com/Mind23-2/MindCode-3/tree/main/HMR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"human-mesh-recovery","task_name":"Human Mesh Recovery"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"graph-transformer","method_name":"Graph Transformer"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"lapeigen","method_name":"LapEigen"},{"method_slug":"laplacian-pe","method_name":"Laplacian PE"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-sloper4d","task":"3D Human Pose Estimation","dataset":"SLOPER4D","model":"LiDAR-HMR","rank_in_archive_order":1,"of":4,"metrics":{"Average MPJPE (mm)":"50.70"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-sloper4d","task":"3D Human Pose Estimation","dataset":"SLOPER4D","model":"Graphormer","rank_in_archive_order":2,"of":4,"metrics":{"Average MPJPE (mm)":"77.1"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2311.11971","atlas_url":"https://app.syntology.ai/?focus=2311.11971","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}