Finding point correspondences between two views is generally based on the matching of local photometric descriptors. A subsequent geometric constraint ensures that the set of matching points is consistent with a realistic camera motion. Starting from a paper by Moisan and Stival, we propose an a contrario model for matching interest points based on descriptor similarity and geometric constraints. The resulting algorithm has adaptive matching thresholds and is able to detect point correspondences whose associated descriptors are not the first nearest neighbor. We also discuss the specific difficulties raised by images containing repeated patterns which are likely to introduce correspondences beyond the nearest neighbor.