  Video-based gait recognition has achieved impressive results in constrained
scenarios. However, visual cameras neglect human 3D structure information,
which limits the feasibility of gait recognition in the 3D wild world. Instead
of extracting gait features from images, this work explores precise 3D gait
features from point clouds and proposes a simple yet efficient 3D gait
recognition framework, termed LidarGait. Our proposed approach projects sparse
point clouds into depth maps to learn the representations with 3D geometry
information, which outperforms existing point-wise and camera-based methods by
a significant margin. Due to the lack of point cloud datasets, we built the
first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by
a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from
1,050 subjects and covers many variations, including visibility, views,
occlusions, clothing, carrying, and scenes. Extensive experiments show that (1)
3D structure information serves as a significant feature for gait recognition.
(2) LidarGait outperforms existing point-based and silhouette-based methods by
a significant margin, while it also offers stable cross-view results. (3) The
LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor
environment. The source code and dataset have been made available at
https://lidargait.github.io.
