Unsupervised learning of 3D shape feature is a challenging yet important problem for organizing a large collection of 3D shape models that do not have annotations. Recently proposed neural network-based approaches attempt to learn meaningful 3D shape feature by autoencoding a single 3D shape representation such as voxel, 3D point set, or multiview 2D images. However, using single shape representation isn't sufficient in training an effective 3D shape feature extractor, as none of existing shape representation can fully describe geometry of 3D shapes by itself. In this paper, we propose to use transcoding across multiple 3D shape representations as the unsupervised method to obtain expressive 3D shape feature. A neural network called Shape Auto-Transcoder (SAT) learns to extract 3D shape features via cross-prediction of multiple heterogeneous 3D shape representations. Architecture and training objective of SAT are carefully designed to obtain effective feature embedding. Experimental evaluation using 3D model retrieval and 3D model classification scenarios demonstrates high accuracy as well as compactness of the proposed 3D shape feature. The code of SAT is available at https://github.com/takahikof/ShapeAutoTranscoder.

对于组织大量没有注释的3D形状模型，无监督学习3D形状特征是一个具有挑战性但重要的问题。最近提出的基于神经网络的方法尝试通过自动编码单个3D形状表示（例如体素，3D点集或多视图2D图像）来学习有意义的3D形状特征。但是，使用单一形状表示法不足以训练有效的3D形状特征提取器，因为现有的形状表示法都无法单独完全描述3D形状的几何形状。在本文中，我们建议使用跨多个3D形状表示的代码转换作为一种无监督的方法来获得富有表现力的3D形状特征。称为形状自动转码器（SAT）的神经网络通过对多个异构3D形状表示的交叉预测来学习提取3D形状特征。SAT的体系结构和培训目标经过精心设计，以实现有效的特征嵌入。使用3D模型检索和3D模型分类方案进行的实验评估证明了所提出的3D形状特征的高精度和紧凑性。SAT的代码可从https://github.com/takahikof/ShapeAutoTranscoder获得。