  It is laborious to manually label point cloud data for training high-quality
3D object detectors. This work proposes a weakly supervised approach for 3D
object detection, only requiring a small set of weakly annotated scenes,
associated with a few precisely labeled object instances. This is achieved by a
two-stage architecture design. Stage-1 learns to generate cylindrical object
proposals under weak supervision, i.e., only the horizontal centers of objects
are click-annotated on bird's view scenes. Stage-2 learns to refine the
cylindrical proposals to get cuboids and confidence scores, using a few
well-labeled object instances. Using only 500 weakly annotated scenes and 534
precisely labeled vehicle instances, our method achieves 85-95% the performance
of current top-leading, fully supervised detectors (which require 3, 712
exhaustively and precisely annotated scenes with 15, 654 instances). More
importantly, with our elaborately designed network architecture, our trained
model can be applied as a 3D object annotator, allowing both automatic and
active working modes. The annotations generated by our model can be used to
train 3D object detectors with over 94% of their original performance (under
manually labeled data). Our experiments also show our model's potential in
boosting performance given more training data. Above designs make our approach
highly practical and introduce new opportunities for learning 3D object
detection with reduced annotation burden.

Computer Vision and Pattern Recognition