This paper addresses the problem of distributed coding of light fields in camera networks. A novel distributed coding scheme with side information is presented, based on spherical image expansion over an overcomplete dictionary of geometric atoms. We propose to model the correlation between views with local geometrical transformations of corresponding features in the sparse representations of different views. We design a Wyner-Ziv encoder by partitioning the dictionary into cosets of dissimilar atoms, with respect to their shape and position on the image. The joint decoder finds pairwise correspondences between atoms in the reference image and atoms in cosets of the Wyner-Ziv image. It selects the most likely correspondence among pairs of atoms that satisfy epipolar geometry constraints. This permits to estimate local transformations between correlated images that eventually help to refine the side information provided by the reference image. Experiments demonstrate that the proposed method is capable of estimating the geometric transformations between views, and hence to reconstruct the Wyner-Ziv image.