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.