Recent years have witnessed the remarkable progress of 3D multi-modality object detection methods based on the Bird's-Eye-View (BEV) perspective. However, most of them overlook the complementary interaction and guidance between LiDAR and camera. In this work, we propose a novel multi-modality 3D objection detection method, with multi-guided global interaction and LiDAR-guided adaptive fusion, named MGAF. Specifically, we introduce sparse depth guidance (SDG) and LiDAR occupancy guidance (LOG) to generate 3D features with sufficient depth and spatial information. The designed semantic segmentation network captures category and orientation prior information for raw point clouds. In the following, an Adaptive Fusion Dual Transformer (AFDT) is developed to adaptively enhance the interaction of different modal BEV features from both global and bidirectional perspectives. Meanwhile, additional downsampling with sparse height compression and multi-scale dual-path transformer (MSDPT) are designed in order to enlarge the receptive fields of different modal features. Finally, a temporal fusion module is introduced to aggregate features from previous frames. Notably, the proposed AFDT is general, which also shows superior performance on other models. Our framework has undergone extensive experimentation on the large-scale nuScenes dataset, Waymo Open Dataset, and long-range Argoverse2 dataset, consistently demonstrating state-of-the-art performance. The code will be released at:https://github.com/xioatian1/MGAF. 3D object detection, multi-modality, multiple guidance, adaptive fusion, BEV representation, autonomous driving.
Fan et al. (Wed,) studied this question.
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