  Most of existing superpixel methods are designed to segment standard planar
images as pre-processing for computer vision pipelines. Nevertheless, the
increasing number of applications based on wide angle capture devices, mainly
generating 360{\deg} spherical images, have enforced the need for dedicated
superpixel approaches. In this paper, we introduce a new superpixel method for
spherical images called SphSPS (for Spherical Shortest Path-based Superpixels).
Our approach respects the spherical geometry and generalizes the notion of
shortest path between a pixel and a superpixel center on the 3D spherical
acquisition space. We show that the feature information on such path can be
efficiently integrated into our clustering framework and jointly improves the
respect of object contours and the shape regularity. To relevantly evaluate
this last aspect in the spherical space, we also generalize a planar global
regularity metric. Finally, the proposed SphSPS method obtains significantly
better performance than both planar and recent spherical superpixel approaches
on the reference 360{\deg} spherical panorama segmentation dataset.
