Texture-Less Object Tracking with Online Training using An RGB-D Camera

Abstract

We propose a texture-less object detection and 3D tracking method which automatically extracts on the fly the information it needs from color images and the corresponding depth maps. While texture-less 3D tracking is not new, it requires a prior CAD model, and real-time methods for detection still have to be developed for robust tracking. To detect the target, we propose to rely on a fast template-based method, which provides an initial estimate of its 3D pose, and we refine this estimate using the depth and image contours information. We automatically extract a 3D model for the target from the depth information. To this end, we developed methods to enhance the depth map and to stabilize the 3D pose estimation. We demonstrate our method on challenging sequences exhibiting partial occlusions and fast motions.