LiDARCapV2: 3D human pose estimation with human–object interaction from LiDAR point clouds

Human–object interactions in open environments are common in the real world. Estimating 3D human pose from data where objects occlude the human is a challenging task in biometrics. However, existing LiDAR-based human motion capture datasets lack occlusion scenarios between humans and objects. To overcome this limitation, we propose LiDARHuman51M, a new human–object interaction dataset captured by LiDAR in long-range outdoor scene. It includes human motion labels acquired by an IMU system and synchronous RGB images. Additionally, we present an occlusion-aware method, LiDARCapV2, for capturing human motion from LiDAR point clouds under human–object interaction settings. Our key insight is to overcome object interference in human feature extraction by introducing a module called AgNoise-Segment. A noise augmentation strategy introduced in the AgNoise-Segment module alleviates the dependency of the segmentation accuracy on the effectiveness of 3D human pose estimations. Furthermore, we propose a skeleton extraction module that integrates features learned from the AgNoise-Segment module and predicts the skeleton locations. Quantitative and qualitative experiments demonstrate that LiDARCapV2 can capture high-quality 3D human motion under human–object interaction settings. Experiments on the KITTI and Waymo datasets demonstrate that our method can be generalized to real-world open scenarios. •We provide a new benchmark for LiDAR-based motion capture with 3D human pose labels.•We propose a LiDAR method for 3D human motion capture during human–object interaction.•Analysis shows LiDAR-based methods excel in long-range and human–object interactions.