三维 ( 3D) 目标检测常见的数据格式为图像和点云，图像数据具有较好的目 标识别能力，点云数据具有准确的空间信息。为了更好地利用图像数据目标识别能力较 强和点云数据空间信息较准确的优点，文中提出了基于点云俯视图重映射的 3D 目标检 测方法 Bird-PointNet。该方法首先将点云编码为俯视图格式，进行目标识别和粗略定 位; 然后将俯视图检测结果映射回点云空间，进行精确检测。在 KITTI 数据集上的俯视 图检测和 3D 检测实验结果表明: 与仅使用点云俯视图编码的基准方法相比，BirdPointNet 方法的 3D 检测准确率更高。

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．