  In this work, we propose "tangent images," a spherical image representation
that facilitates transferable and scalable $360^\circ$ computer vision.
Inspired by techniques in cartography and computer graphics, we render a
spherical image to a set of distortion-mitigated, locally-planar image grids
tangent to a subdivided icosahedron. By varying the resolution of these grids
independently of the subdivision level, we can effectively represent high
resolution spherical images while still benefiting from the low-distortion
icosahedral spherical approximation. We show that training standard
convolutional neural networks on tangent images compares favorably to the many
specialized spherical convolutional kernels that have been developed, while
also scaling efficiently to handle significantly higher spherical resolutions.
Furthermore, because our approach does not require specialized kernels, we show
that we can transfer networks trained on perspective images to spherical data
without fine-tuning and with limited performance drop-off. Finally, we
demonstrate that tangent images can be used to improve the quality of sparse
feature detection on spherical images, illustrating its usefulness for
traditional computer vision tasks like structure-from-motion and SLAM.
