上海科技大学知识管理系统

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. © 2024 Elsevier Ltd