3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection

3D object detection is a vital part of outdoor scene perception. Learning the complete size and accurate positioning of objects from an incomplete point cloud spatial structure is essential to 3D object detection. We propose a novel flexible 3D heatmap auxiliary network for object detection (3D HANet). To obtain complete structure and location information from an incomplete point cloud structure, we propose a 3D heatmap to reflect object information. Also, we design a plug-and-play auxiliary network based on 3D heatmap, which improves the accuracy of the entire detection network without extra computation in the inference stage. We validate the 3D heatmap auxiliary network on the basis of three classic 3D object detection networks: PointPillars, SECOND and SASSD. Experimental results show that our auxiliary network augments the feature extraction ability of the backbone network, which is manifested in that the predicted boxes and the ground truth boxes are more suitable in size and more aligned in direction. Furthermore, we conducted verification experiments on the state-of-the-art detector, CasA, and made a further improvement on the official ranking of KITTI dataset.