Shape from Light Field meets Robust PCA

In this paper we propose a new type of matching term for multi-view stereo reconstruction. Our model is based on the assumption, that if one warps the images of the various views to a common warping-center and considers each warped image as one row in a matrix, then this matrix will have low rank. This also implies, that we assume a certain amount of overlap between the views
after the warping has been performed. Such an assumption is obviously met in the case of light field data, which motivated us to demonstrate the proposed model for this type of data. Our final model is a large scale convex optimization problem, where the low rank minimization is relaxed via the nuclear norm. We present qualitative and quantitative experiments, where the proposed model achieves excellent results.