Surface Networks via General Covers
This addresses the problem of applying deep learning to geometric data for researchers in computer vision and graphics, offering an incremental improvement over existing spherical parameterizations.
The paper tackled sphere-type surface learning by developing a novel surface-to-image representation based on a covering map, enabling adaptation of CNN models to surfaces with low distortion. It achieved state-of-the-art or comparable results on shape retrieval, classification, and segmentation tasks.
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. Specifically, for the use case of learning spherical signals, our representation provides a low distortion alternative to several popular spherical parameterizations used in deep learning. We have used the surface-to-image representation to apply standard CNN architectures to 3D models as well as spherical signals. We show that our method achieves state of the art or comparable results on the tasks of shape retrieval, shape classification and semantic shape segmentation.