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

Understand

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.