Cubistic Representation for Real-time 3D Shape and Pose Estimation of Unknown Rigid Object

This paper introduces Cubistic Representation as a novel 3D surface shape model. Cubistic representation is a set of 3D surface fragments; each fragment contains subject's 3D surface shape and its color and redundantly covers the subject surface. By laminating these fragments using a given pose parameter, the subject's appearance can be synthesized. Using cubistic representation, we propose a real-time 3D rigid object tracking approach by acquiring the 3D surface shape and its pose simultaneously. We use the particle filter scheme for both shape and pose estimation; each fragment is used as a partial shape hypothesis and is sampled and refined by a particle filter. We also use the RANSAC algorithm to remove wrong fragments as outliers to refine the shape. We also implemented an online demonstration system with GPU and a Kinect sensor and evaluated the performance of our approach in a real environment.