  The performance of existing point cloud-based 3D object detection methods
heavily relies on large-scale high-quality 3D annotations. However, such
annotations are often tedious and expensive to collect. Semi-supervised
learning is a good alternative to mitigate the data annotation issue, but has
remained largely unexplored in 3D object detection. Inspired by the recent
success of self-ensembling technique in semi-supervised image classification
task, we propose SESS, a self-ensembling semi-supervised 3D object detection
framework. Specifically, we design a thorough perturbation scheme to enhance
generalization of the network on unlabeled and new unseen data. Furthermore, we
propose three consistency losses to enforce the consistency between two sets of
predicted 3D object proposals, to facilitate the learning of structure and
semantic invariances of objects. Extensive experiments conducted on SUN RGB-D
and ScanNet datasets demonstrate the effectiveness of SESS in both inductive
and transductive semi-supervised 3D object detection. Our SESS achieves
competitive performance compared to the state-of-the-art fully-supervised
method by using only 50% labeled data. Our code is available at
https://github.com/Na-Z/sess.
