  Training 3D object detectors for autonomous driving has been limited to small
datasets due to the effort required to generate annotations. Reducing both task
complexity and the amount of task switching done by annotators is key to
reducing the effort and time required to generate 3D bounding box annotations.
This paper introduces a novel ground truth generation method that combines
human supervision with pretrained neural networks to generate per-instance 3D
point cloud segmentation, 3D bounding boxes, and class annotations. The
annotators provide object anchor clicks which behave as a seed to generate
instance segmentation results in 3D. The points belonging to each instance are
then used to regress object centroids, bounding box dimensions, and object
orientation. Our proposed annotation scheme requires 30x lower human annotation
time. We use the KITTI 3D object detection dataset to evaluate the efficiency
and the quality of our annotation scheme. We also test the the proposed scheme
on previously unseen data from the Autonomoose self-driving vehicle to
demonstrate generalization capabilities of the network.
