DGA-Occ: Distance-Guided and Gated-Adaptive Fusion for Multimodal 3D Semantic Occupancy Prediction

Existing multimodal 3D semantic occupancy prediction methods typically perform feature fusion only in local voxel space or global BEV space, failing to account for cross-modal spatial biases and neglecting the complementary advantages of global and local collaborative fusion. To address this, this paper proposes a global-local collaborative multimodal fusion framework based on distance guidance and gating adaptation. Specifically, the gating-adaptive local fusion(GALF) achieves reliable local feature interaction in geometrically aligned voxel space through bidirectional cross-modal nearest neighbor association and learnable gating modulation; the dual-geometric interactive fusion(DGIF) combines cross-modal interaction alignment, camera-LiDAR dual-source geometric priors, and distance-aware attention to achieve spatially adaptive global fusion. Furthermore, the Mamba Split Block jointly models global structure and local details through frequency-aware decomposition, and the local-global fusion module(LGFM) further promotes bidirectional collaboration between camera BEV semantics and LiDAR 3D geometry. Experimental results show that the proposed method achieves mIoU of 54.46 and 25.9 on the Occ3D-nuScenes and nuScenes-Occupancy datasets, respectively, validating the effectiveness of the proposed framework and its components.