  Object detection and semantic segmentation with the 3D lidar point cloud data
require expensive annotation. We propose a data augmentation method that takes
advantage of already annotated data multiple times. We propose an augmentation
framework that reuses real data, automatically finds suitable placements in the
scene to be augmented, and handles occlusions explicitly. Due to the usage of
the real data, the scan points of newly inserted objects in augmentation
sustain the physical characteristics of the lidar, such as intensity and
raydrop. The pipeline proves competitive in training top-performing models for
3D object detection and semantic segmentation. The new augmentation provides a
significant performance gain in rare and essential classes, notably 6.65%
average precision gain for "Hard" pedestrian class in KITTI object detection or
2.14 mean IoU gain in the SemanticKITTI segmentation challenge over the state
of the art.
