We represent 3D shape by structured 2D representations of fixed length making it feasible to apply well investigated 2D convolutional neural networks (CNN) for both discriminative and geometric tasks on 3D shapes. We first provide a general introduction to such structured descriptors, analyze their different forms and show how a simple 2D CNN can be used to achieve good classification result. With a specialized classification network for images and our structured representation, we achieve the classification accuracy of 99.7\% in the ModelNet40 test set - improving the previous state-of-the-art by a large margin. We finally provide a novel framework for performing the geometric task of 3D segmentation using 2D CNNs and the structured representation - concluding the utility of such descriptors for both discriminative and geometric tasks.

翻译：我们通过固定长度的结构化二维表示来表征三维形状，从而使得对三维形状进行分类和几何任务时，能够利用已充分研究的二维卷积神经网络（CNN）。首先，我们对此类结构化描述子进行了一般性介绍，分析其不同形式，并展示了如何通过简单的二维CNN获得良好的分类结果。借助针对图像的专用分类网络和我们的结构化表示，我们在ModelNet40测试集上达到了99.7%的分类准确率——较之前的最优结果有显著提升。最后，我们提出了一个新颖框架，通过二维CNN和结构化表示执行三维分割这一几何任务，从而论证了此类描述子在判別与几何任务中的实用性。