3D Object Detection Based on Improved Frustum PointNet

An improved F-PointNet (Frustum PointNet) for 3D target detection on image and lidar point cloud data is proposed. First, the 2D target detection model of the image is used to extract 2D region of the target, and it is mapped to the point cloud data to obtain the candidate region of the target. Then, the 3D target mask of the candidate region is predicted. Finally, the 3D target is detected by using mask. When the mask is predicted, the proposed wide-threshold mask processing is used to reduce the information loss of the original network, the attention mechanism is added to obtain the points and channel layers that require attention, the Focal Loss can solve the imbalance between the target and the background problem. Through multiple comparison experiments, it is proved that wide-threshold mask processing can improve the accuracy of 3D target detection, and the attention mechanism and Focal Loss can improve the accuracy of prediction.