  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/
