Weakly Supervised 3D Object Detection from Lidar Point Cloud

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 in bird’s view scenes. Stage-2 learns to refine the cylindrical proposals to get cuboids and confidence scores, using a few well-labeled 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 (requiring 3, 712 exhaustively and precisely annotated scenes with 15, 654 instances). Moreover, with our elaborately designed network architecture, our trained model can be applied as a 3D object annotator, supporting both automatic and active (human-in-the-loop) working modes. The annotations generated by our model can be used to train 3D object detectors, achieving over 94% of their original performance (with manually labeled training data). Our experiments also show our model’s potential in boosting performance when given more training data. Above designs make our approach highly practical and introduce new opportunities for learning 3D object detection at reduced annotation cost.