Multi-View Frustum Pointnet for Object Detection in Autonomous Driving
Pei Cao, Hao Chen, Ye Zhang, Gang Wang · 2019
LIDAR point cloud and RGB images are often used for object detection in autonomous driving scenarios. This paper develops a multi-view version of Frustum PointNet (F-PointNet), to be called MVFP to reduce the rate of missed detection in F-PointNet by adding auxiliary bird's eye view (BEV) detection part. In processing MVFP, initial object detection results are obtained from F-PointNet by combining the RGB image and raw LIDAR point cloud. Simultaneously, raw LIDAR point cloud is encoded into BEV feature maps, from which 2D bounding boxes are predicted. In missed detection judgement, the intersection over union (IoU) is used as a criteria for the matching of preliminary object detection results from F-PointNet and BEV maps prediction results. 2D boxes belonging to missed-detected objects from BEV maps are projected to the pipeline of F-PointNet until all the objects in BEV maps find a matching detection result in the set of F-PointNet detection results. To evaluate the performance of MVFP, 3D object detection experiments are conducted on KITTI benchmark. The experiment results demonstrate that MVFP outperforms the original F-PointNet by 5% and 4% higher recall on the hard mode of pedestrian and cyclist.