Papers › LiDAR-HMR: 3D Human Mesh Recovery from LiDAR

LiDAR-HMR: 3D Human Mesh Recovery from LiDAR

20 Nov 2023arXiv:2311.11971archive 2025-07-28

Bohao Fan, Wenzhao Zheng, Jianjiang Feng, Jie zhou

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/

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Code

soullessrobot/lidar-hmr officialmentioned in papermentioned on GitHubpytorch report

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Tasks

3D Human Pose Estimation Human Mesh Recovery

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
3D Human Pose Estimation	SLOPER4D	LiDAR-HMR	Average MPJPE (mm)	50.70	#1 of 4	Archive leaderboard	report
3D Human Pose Estimation	SLOPER4D	Graphormer	Average MPJPE (mm)	77.1	#2 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Graph Transformer Label Smoothing LapEigen Laplacian PE Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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