Geodesic Convolutional Neural Networks on Riemannian Manifolds

Jonathan Masci, Davide Boscaini, Michael M. Bronstein, Pierre Vandergheynst; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2015, pp. 37-45

Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we introduce Geodesic Convolutional Neural Networks (GCNN), a generalization of the convolutional neural networks (CNN) paradigm to non-Euclidean manifolds. Our construction is based on a local geodesic system of polar coordinates to extract "patches", which are then passed through a cascade of filters and linear and non-linear operators. The coefficients of the filters and linear combination weights are optimization variables that are learned to minimize a task-specific cost function. We use ShapeNet to learn invariant shape features, allowing to achieve state-of-the-art performance in problems such as shape description, retrieval, and correspondence.