In this paper, we present a superpixel generation method for spherical images, which cover 360° field-of-view. Unlike previous works that directly use existing superpixel algorithms on unrolled spherical images, our approach explicitly considers the geometry for spherical images and uses sphere as the underlying representation. For quantitative evaluation, we make a spherical image segmentation database by transforming Berkeley segmentation dataset to the spherical domain. Experimental results show that our method can get better performance in terms of adherence to image boundaries and spherical size variance. What's more, superpixels generated by our method all have closed contours.
['Qiang Zhao', 'Liang Wan', 'Jiawan Zhang']
Spherical superpixel segmentation