  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.
