  Depth estimation is usually ill-posed and ambiguous for monocular
camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate
depth information in long-range scenes, it can benefit both the global
localization of individuals and the 3D pose estimation by providing rich
geometry features. Motivated by this, we propose a monocular camera and single
LiDAR-based method for 3D multi-person pose estimation in large-scale scenes,
which is easy to deploy and insensitive to light. Specifically, we design an
effective fusion strategy to take advantage of multi-modal input data,
including images and point cloud, and make full use of temporal information to
guide the network to learn natural and coherent human motions. Without relying
on any 3D pose annotations, our method exploits the inherent geometry
constraints of point cloud for self-supervision and utilizes 2D keypoints on
images for weak supervision. Extensive experiments on public datasets and our
newly collected dataset demonstrate the superiority and generalization
capability of our proposed method.
