Stay in the loop.

Subscribe to our newsletter for a weekly update on the latest podcast, news, events, and jobs postings.

Spherical CNNs.

RSS Source
Taco S. Cohen, Mario Geiger, Jonas Koehler, Max Welling

Convolutional Neural Networks (CNNs) have become the method of choice forlearning problems involving 2D planar images. However, a number of problems ofrecent interest have created a demand for models that can analyze sphericalimages. Examples include omnidirectional vision for drones, robots, andautonomous cars, molecular regression problems, and global weather and climatemodelling. A naive application of convolutional networks to a planar projectionof the spherical signal is destined to fail, because the space-varyingdistortions introduced by such a projection will make translational weightsharing ineffective.In this paper we introduce the building blocks for constructing sphericalCNNs. We propose a definition for the spherical cross-correlation that is bothexpressive and rotation-equivariant. The spherical correlation satisfies ageneralized Fourier theorem, which allows us to compute it efficiently using ageneralized (non-commutative) Fast Fourier Transform (FFT) algorithm. Wedemonstrate the computational efficiency, numerical accuracy, and effectivenessof spherical CNNs applied to 3D model recognition and atomization energyregression.