Convolutional neural networks have achieved extraordinary results in many computer vision and pattern recognition applications; however, their adoption in the computer graphics and geometry processing communities is limited due to the non-Euclidean structure of their data. In this paper, we propose Anisotropic Convolutional Neural Network (ACNN), a generalization of classical CNNs to non-Euclidean domains, where classical convolutions are replaced by projections over a set of oriented anisotropic diffusion kernels. We use ACNNs to effectively learn intrinsic dense correspondences between deformable shapes, a fundamental problem in geometry processing, arising in a wide variety of applications. We tested ACNNs performance in challenging settings, achieving state-of-the-art results on recent correspondence benchmarks.
|Number of pages||9|
|Journal||Advances in Neural Information Processing Systems|
|State||Published - 2016|
|Event||30th Annual Conference on Neural Information Processing Systems, NIPS 2016 - Barcelona, Spain|
Duration: 5 Dec 2016 → 10 Dec 2016