Radar-camera fusion for 3D object detection with aggregation transformer

In recent years, with the continuous development of autonomous driving, monocular 3D object detection has garnered increasing attention as a crucial research topic. However, the precision of 3D object detection is impeded by the limitations of monocular camera sensors, which struggle to capture accurate depth information. To address this challenge, a novel Aggregation Transformer Network (ATNet) is introduced, featuring Cross-Attention based Positional Aggregation and Dual Expansion-Squeeze based Channel Aggregation. The proposed ATNet adaptively fuses radar and camera data at both positional and channel levels. Specifically, the Cross-Attention based Positional Aggregation leverages camera-radar information to compute a non-linear attention coefficient, which reinforces salient features and suppresses irrelevant ones. The Dual Expansion-Squeeze based Channel Aggregation utilizes refined processing techniques to integrate radar and camera data adaptively at the channel level. Furthermore, to enhance feature-level fusion, we propose a multi-scale radar-camera fusion strategy that integrates radar information across multiple stages of the camera subnet’s backbone, allowing for improved object detection across various scales. Extensive experiments conducted on the widely-used nuScenes dataset validate that our proposed Aggregation Transformer, when integrated into superb monocular 3D object detection models, delivers promising results compared to existing methods.