Surface Networks via General Covers
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Developing deep learning techniques for geometric data is an active and fruitful research area. This paper tackles the problem of sphere-type surface learning by developing a novel surface-to-image representation. Using this representation we are able to quickly adapt successful CNN models to the surface setting. The surface-image representation is based on a covering map from the image domain to the surface. Namely, the map wraps around the surface several times, making sure that every part of the surface is well represented in the image. Differently from previous surface-to-image representations we provide a low distortion coverage of all surface parts in a single image. We have used the surface-to-image representation to apply standard CNN models to the problem of semantic shape segmentation and shape retrieval, achieving state of the art results in both.
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