Texture-Less Planar Object Detection and Pose Estimation Using Depth-Assisted Rectification of Contours

This paper presents a method named Depth-Assisted Rectification of Contours (DARC) for detection and pose estimation of textureless planar objects using RGB-D cameras. It consists in matching contours extracted from the current image to previously acquired template contours. In order to achieve invariance to rotation, scale and perspective distortions, a rectified representation of the contours is obtained using the available depth information. DARC requires only a single RGB-D image of the planar objects in order to estimate their pose, opposed to some existing approaches that need to capture a number of views of the target object. It also does not require to generate warped versions of the templates, which is commonly needed by existing object detection techniques. It is shown that the DARC method runs in real-time and its detection and pose estimation quality are suitable for augmented reality applications.