GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection

• URL: http://arxiv.org/abs/2411.00340v1
• Date: Fri, 01 Nov 2024 03:40:24 GMT
• Title: GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection
• Authors: Xiaotian Li, Baojie Fan, Jiandong Tian, Huijie Fan,
• Abstract summary: We propose a novel multi-modality 3D objection detection method, named GAFusion, with LiDAR-guided global interaction and adaptive fusion. GAFusion achieves state-of-the-art 3D object detection results with 73.6$%$ mAP and 74.9$%$ NDS on the nuScenes test set.
• Score: 36.37236815038332
• License:
• Abstract: 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.

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