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LiDAR can capture accurate depth information in large-scale scenarios withoutthe effect of light conditions, and the captured point cloud containsgait-related 3D geometric properties and dynamic motion characteristics. Wemake the first attempt to leverage LiDAR to remedy the limitation ofview-dependent and light-sensitive camera for more robust and accurate gaitrecognition. In this paper, we propose a LiDAR-camera-based gait recognitionmethod with an effective multi-modal feature fusion strategy, which fullyexploits advantages of both point clouds and images. In particular, we proposea new in-the-wild gait dataset, LiCamGait, involving multi-modal visual dataand diverse 2D/3D representations. Our method achieves state-of-the-artperformance on the new dataset. Code and dataset will be released when thispaper is published.

LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors