We propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. Our key idea is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HRNet to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, Our network is concatenated with simple task-specific back-ends and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods. For fine-grained shape segmentation on the PartNet dataset, our method even surpasses existing supervised methods by a large margin.

Abstracting man-made 3D objects as assemblies of primitives, i.e. , shape abstraction, is an important task in 3D shape understanding and analysis. In this paper, we propose an unsupervised learning method for automatically constructing compact and expressive shape abstractions of 3D objects in a class. The key idea of our approach is an adaptive hierarchical cuboid representation that abstracts a 3D shape with a set of parametric cuboids adaptively selected from a hierarchical and multi-level cuboid representation shared by all objects in the class. The adaptive hierarchical cuboid abstraction offers a compact representation for modeling the variant shape structures and their coherence at different abstraction levels. Based on this representation, we design a convolutional neural network (CNN) for predicting the parameters of each cuboid in the hierarchical cuboid representation and the adaptive selection mask of cuboids for each input 3D shape. For training the CNN from an unlabeled 3D shape collection, we propose a set of novel loss functions to maximize the approximation quality and compactness of the adaptive hierarchical cuboid abstraction and present a progressive training scheme to refine the cuboid parameters and the cuboid selection mask effectively. We evaluate the effectiveness of our approach on various 3D shape collections and demonstrate its advantages over the existing cuboid abstraction approach. We also illustrate applications of the resulting adaptive cuboid representations in various shape analysis and manipulation tasks.