This paper presents a method named Depth-Assisted Rectification of Patches (DARP), which exploits depth information available in RGB-D consumer devices to improve keypoint matching of perspectively distorted images. This is achieved by generating a projective rectification of a patch around the keypoint, which is normalized with respect to perspective distortions and scale. The DARP method runs in real-time and can be used with any local feature detector and descriptor. Evaluations with planar and non-planar scenes show that DARP can obtain better results than existing keypoint matching approaches in oblique poses.