Cascade fusion of multi-modal and multi-source feature fusion by the attention for three-dimensional object detection

Three dimensional (3D) object detection using the camera and light detection and ranging (LiDAR) fusion model has received much attention for meeting the environmental sensing requirement in autonomous vehicles. Due to the intrinsic data difference and the uneven distribution of LiDAR data, integrating the camera and LiDAR to solve the problem of the uneven spatial distribution of LiDAR data is still challenging. This paper proposes a novel 3D detector, which decorates the point cloud voxels with the semantic features at multi-scale and fuses the multi-source features by cross-attention to improve the detection performance. Specifically, the multi-modal fusion aims to maintain the cross-modal consistency by using the frustum model to construct the correspondence between point voxels and image pixels at multi-scale. To fully exploit the geometric constraints of voxels, we developed a adaptive sampling radius module to dynamically select the sampling radius of the voxel set abstraction module based on softmax. Moreover, we propose a multi-source fusion module which utilizes the cross-attention and takes the raw point cloud’s spatial distribution as the clue to fuse the features of point clouds, bird’s eye view information and the aggregated voxels to obtain the multi-scale and multi-source fusion features. Finally, region proposal network is adopted to generate and refine the 3D bbox and class prediction based on the fusion feature and bird’s eye view feature. Extensive experiments are conducted on the two publicly available 3D object datasets proposed by Karlsruhe Institute of technology and Toyota Technological Institute (KITTI) and the nuScenes. The proposed model reaches 86.27% mean Average Precision (mAP) for car on KITTI and obtains 6.56% gains than PointPainting. Moreover, our model reaches 70.70% mAP on nuScens and improves 2.61% than Transfusion. Furthermore, comprehensive ablation experiments are conducted to validate the effects and contribution of the different components of the proposed model.