Abstract: Image and point cloud are the common data formats for 3D object detection，for images have a superior object recognition capability and point clouds contain accurate spatial information． In order to utilize the above mentioned advantages of both images and point clouds，a 3D object detection method named Bird-PointNet based on bird's eye view of point cloud remapping approach was proposed． First，point cloud was encoded into bird's eye view format for object recognition and rough positioning． Then the results from bird's eye view detection was remapped into the point cloud's space for precise detection． Experiments on the BEV detection benchmark and the 3D detection benchmark of KITTI dataset have demonstrated that the proposed Bird-PointNet method has a higher accuracy of 3D detection，compared with the baseline method that only with bird's eye view coding of point cloud．

Key words: object detection, autopilot driving, point cloud, bird's eye view