PointPillars is a 3D object detector from LiDAR point cloud, please refer to the paper for more details:
Lang, Alex H., et al. "PointPillars: Fast encoders for object detection from point clouds." arXiv preprint arXiv:1812.05784(2018).

1. We train the PointPillars on Kitti 3D object detection dataset (only trained for class “car”). Total training step: 296960.

2. We randomly remove LiDAR points from validation subset to simulate an attack that affects the LiDAR receives less points. Then, we evaluate the robustness of the PointPillars under such an attack.

   In the following table, we report the AP@0.70 of PointPillars on car 3D location detection from LiDAR point cloud.

   Scheme	Ratio of Points	Easy	Moderate	Hard
   PointPillars	100% (ori)	85.39	76.56	79.04
   PointPillars	80%	80.63	70.58	68.23
   PointPillars	60%	80.16	69.87	67.29
   PointPillars	40%	77.36	66.12	60.63
   PointPillars	20%	64.97	52.73	47.31

Here, we visualize several samples:

• Green rectangles are ground truth.
• Red rectangles are PointPillars detection results.
• Purple points are ground truth points of cars.

Animation	Front View Camera	100% Points	80% Points	60% Points	40% Points	20% Points