  We investigate the role of representations and architectures for classifying
3D shapes in terms of their computational efficiency, generalization, and
robustness to adversarial transformations. By varying the number of training
examples and employing cross-modal transfer learning we study the role of
initialization of existing deep architectures for 3D shape classification. Our
analysis shows that multiview methods continue to offer the best generalization
even without pretraining on large labeled image datasets, and even when trained
on simplified inputs such as binary silhouettes. Furthermore, the performance
of voxel-based 3D convolutional networks and point-based architectures can be
improved via cross-modal transfer from image representations. Finally, we
analyze the robustness of 3D shape classifiers to adversarial transformations
and present a novel approach for generating adversarial perturbations of a 3D
shape for multiview classifiers using a differentiable renderer. We find that
point-based networks are more robust to point position perturbations while
voxel-based and multiview networks are easily fooled with the addition of
imperceptible noise to the input.
