AFMCT: adaptive fusion module based on cross-modal transformer block for 3D object detection

Lidar and camera are essential sensors for environment perception in autonomous driving. However, fully fusing heterogeneous data from multiple sources remains a non-trivial challenge. As a result, 3D object detection based on multi-modal sensor fusion are often inferior to single-modal methods only based on Lidar, which indicates that multi-sensor machine vision still needs development. In this paper, we propose an adaptive fusion module based on cross-modal transformer block(AFMCT) for 3D object detection by utilizing a bidirectional enhancing strategy. Specifically, we first enhance image feature by extracting an attention-based point feature based on a cross-modal transformer block and linking them in a concatenation fashion, followed by another cross-modal transformer block acting on the enhanced image feature to strengthen the point feature with image semantic information. Extensive experiments operated on the 3D detection benchmark of the KITTI dataset reveal that our proposed structure can significantly improve the detection accuracy of Lidar-only methods and outperform the existing advanced multi-sensor fusion modules by at least 0.45%, which indicates that our method might be a feasible solution to improving 3D object detection based on multi-sensor fusion.