SCMF-Net: Sparse Self-Attention Driven Cross-Modal Fusion for Robust Detection in Complex Road Scenes

This paper introduces SCMF-Net (Sparse Cross-Modal Fusion Network), a lightweight multimodal perception framework designed to enhance representation quality and inference efficiency while minimizing computational overhead. To address the sparsity and irregular distribution of LiDAR point clouds, an intensity-aware depth encoding strategy is proposed to enhance the structural cues in the depth modality. Additionally, a dual-branch backbone is employed to further strengthen feature extraction. Building upon this, FFLSA (Feature Fusion Local Self-Attention) is introduced to enable efficient cross-modal fusion. FFLSA leverages Self-Attention Clustering (SAC) to identify salient cross-modal regions, and…

• NS
  Natural Science Foundation of Hainan Province

  Award: 2021JC0004
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2023B1515120066