  The lack of fine-grained 3D shape segmentation data is the main obstacle to
developing learning-based 3D segmentation techniques. We propose an effective
semi-supervised method for learning 3D segmentations from a few labeled 3D
shapes and a large amount of unlabeled 3D data. For the unlabeled data, we
present a novel multilevel consistency loss to enforce consistency of network
predictions between perturbed copies of a 3D shape at multiple levels:
point-level, part-level, and hierarchical level. For the labeled data, we
develop a simple yet effective part substitution scheme to augment the labeled
3D shapes with more structural variations to enhance training. Our method has
been extensively validated on the task of 3D object semantic segmentation on
PartNet and ShapeNetPart, and indoor scene semantic segmentation on ScanNet. It
exhibits superior performance to existing semi-supervised and unsupervised
pre-training 3D approaches. Our code and trained models are publicly available
at https://github.com/isunchy/semi_supervised_3d_segmentation.
