This paper deals with the problem of computing a semantic segmentation of an image via label transfer from an already labeled image set.
In particular it proposes a method that takes advantage of sparse 3D structure to infer the category of superpixel in the novel image.
The label assignment is computed by a Markov random field that has the superpixels of the image as nodes.
The data term combines labeling proposals from the appearance of the superpixel and from the 3D structure, while the pairwise term incorporates spatial context, both in the image and in 3D space. Exploratory results indicate that 3D structure, albeit sparse, improves
the process of label transfer.
