We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects: (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semantic features of images and the geometric features of point clouds. (2) The optimization of traditional IoU is not equal to the regression loss of bounding boxes, resulting in biased back-propagation for non-overlapping cases. In this work, we propose a cascaded cross-modality fusion network (CCFNet), which includes a cascaded multi-scale fusion module (CMF) and a novel center 3D IoU loss to resolve these two issues. Our CMF module is developed to reinforce the discriminative representation of objects by reasoning the relation of corresponding LIDAR geometric capability and RGB semantic capability of the object from two modalities. Specifically, CMF is added in a cascaded way between the RGB and LIDAR streams, which selects salient points and transmits multi-scale point cloud features to each stage of RGB streams. Moreover, our center 3D IoU loss incorporates the distance between anchor centers to avoid the oversimple optimization for non-overlapping bounding boxes. Extensive experiments on the KITTI benchmark have demonstrated that our proposed approach performs better than the compared methods.
Keywords: 3D object detection; LIDAR; multi-sensor fusion; point cloud processing.