Point-HRRP-Net: A Deep Fusion Framework via Bi-Directional Cross-Attention for Space Object Classification Using HRRP and Point Cloud

High-Resolution Range Profile (HRRP)-based space object classification is severely limited by aspect sensitivity. Inspired by the intrinsic complementarity between HRRP and LiDAR point clouds, this work investigates the feasibility and effectiveness of fusing these two modalities to address this limitation. We propose the Point-HRRP-Net framework. This framework employs dual-stream extractors to independently encode HRRP electromagnetic signatures and 3D point cloud geometric topologies. Subsequently, a Bi-Directional Cross-Attention (Bi-CA) mechanism is designed to fuse the two modalities. To enable information interaction, this mechanism utilizes point-to-point attention to correlate radar scattering features with 3D geometric points, thereby constructing a comprehensive target representation. Due to data scarcity, we constructed a paired simulation dataset for evaluation. Experimental results demonstrate that the proposed framework consistently outperforms its constituent single-modality baselines. The model achieves 57.67% accuracy on the 180 split and demonstrates generalization capability to unseen viewpoints. Ablation studies further validate the efficacy of the Bi-CA mechanism and the selected feature extractors. Finally, we assess the potential sim-to-real discrepancies and evaluate deployment feasibility across various hardware platforms.