GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection
Authors: Xiaotian Li, Baojie Fan, Jiandong Tian, Huijie Fan

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 named GAFusion with LiDAR-guided global interaction and adaptive fusion. Specifically we introduce sparse depth guidance (SDG) and LiDAR occupancy guidance (LOG) to generate 3D features with sufficient depth information. In the following LiDAR-guided adaptive fusion transformer (LGAFT) is developed to adaptively enhance the interaction of different modal BEV features from a global perspective. Meanwhile additional downsampling with sparse height compression and multi-scale dual-path transformer (MSDPT) are designed to enlarge the receptive fields of different modal features. Finally a temporal fusion module is introduced to aggregate features from previous frames. GAFusion achieves state-of-the-art 3D object detection results with 73.6% mAP and 74.9% NDS on the nuScenes test set.