Although unsupervised feature learning has demonstrated its advantages to
reducing the workload of data labeling and network design in many fields,
existing unsupervised 3D learning methods still cannot offer a generic network
for various shape analysis tasks with competitive performance to supervised
methods. In this paper, we propose an unsupervised method for learning a
generic and efficient shape encoding network for different shape analysis
tasks. The key idea of our method is to jointly encode and learn shape and
point features from unlabeled 3D point clouds. For this purpose, we adapt
HR-Net 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, the
network is concatenated with simple task-specific back-end layers and
fine-tuned for different shape analysis tasks. We evaluate the efficacy and
generality of our method and validate our network and loss design 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, especially in tasks
with a small labeled dataset. For fine-grained shape segmentation, our method
even surpasses existing supervised methods by a large margin.

本文提出了一种用于不同形状分析任务的通用和高效形状编码网络的无监督方法，具体方法是联合编码和学习来自未标记的三维点云的形状和点特征，使用基于八叉树的卷积神经网络来进行编码，并设计了一个简单而高效的多分辨率实例判别损失，本方法不仅表现出了所有无监督方法中最佳的性能，还在小标记数据集的任务中表现出了与监督方法有竞争力的性能，甚至在细粒度形状分割任务中超过了现有的监督方法。