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

Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation