  3D object detection plays an important role in autonomous driving and other
robotics applications. However, these detectors usually require training on
large amounts of annotated data that is expensive and time-consuming to
collect. Instead, we propose leveraging large amounts of unlabeled point cloud
videos by semi-supervised learning of 3D object detectors via temporal graph
neural networks. Our insight is that temporal smoothing can create more
accurate detection results on unlabeled data, and these smoothed detections can
then be used to retrain the detector. We learn to perform this temporal
reasoning with a graph neural network, where edges represent the relationship
between candidate detections in different time frames. After semi-supervised
learning, our method achieves state-of-the-art detection performance on the
challenging nuScenes and H3D benchmarks, compared to baselines trained on the
same amount of labeled data. Project and code are released at
https://www.jianrenw.com/SOD-TGNN/.
