PePNet: Pose-Enhanced Point Cloud Network for LiDAR-Based Human Action Recognition in Outdoor Long-Range Scenarios

With potential applications in robotics and autonomous vehicles, LiDAR-based human action recognition (HAR) in outdoor long-range scenarios is challenging due to the degradation of point cloud density with distance and the simultaneous motion of humans and sensors. To address these issues, we propose the Pose-Enhanced Point Cloud Network (PePNet), a distance-aware framework for long-range HAR. As the core component, the Pose-Enhanced Point Cloud Block (PeP Block) integrates three modules: a Dynamic Enhancement Module that mitigates point cloud sparsity at long distances by generating supplementary points from motion cues, a Pose Prompter Module that introduces pose priors, and an Adaptive Point Selection Module that suppresses irrelevant body-part movements. We further design a Spatiotemporal Tube Embedding (ST-Tube), combined with the Mamba state space model, to capture long-range dependencies and complex motion dynamics. In addition, we construct Momo, a large-scale LiDAR-based HAR dataset that focuses on long-range (2-30 m) outdoor scenarios where sparse point clouds and simultaneous human-sensor motion pose prominent challenges, complementing existing benchmarks by providing a dedicated evaluation platform for long-range outdoor HAR. Experimental results show that PePNet achieves consistent performance gains over existing methods on Momo. Moreover, the proposed PeP Block can serve as a plug-and-play module to enhance other point cloud action recognition frameworks in long-range outdoor settings. The code is available at https://github.com/Shark0-0/PePNet.