PointPillars is an efficient network for real-time 3D object detection on point cloud. Trained on the nuScenes dataset, this model gives 3D bounding boxes and speed prediction for ten classes (including some kinds of vehicles, pedestrian, barrier, and traffic cone) in the surround-view range. With multi-sweep point clouds as input, PointPillars can achieve higher accuracy of 3D object detection and speed estimation at the cost of increasing complexity of the pre-processing part.
Figure 1. Pointpillars_nuscenes Example
The following table lists the Pointpillars_nuscenes models supported by the Vitis AI library.