Reconstruction of 3D deformable objects using a single Kinect sensor

Abstract

3D object reconstruction has multiple applications in areas such as medicine, robotics, virtual reality, video games, reverse engineering, human computer interaction among others. This work addresses the following research problem: design of an algorithm for accurate real-time reconstruction of 3D deformable objects using a single Kinect sensor without restricting too much user or camera motion. Prior knowledge of the object shape is not used, allowing a general scanning of the object with free deformations. The reconstruction process consists of the following steps: capture in time of RGB-D information with a Kinect sensor, registration using a modified iterative closest point algorithm, and dynamic construction and refinement of a dense 3D object model. To improve the model quality, segmentation of the desired object from background based on a depth-error analysis is employed. The performance of the algorithm is evaluated using experimentally validated data and compared with state-of-the-art techniques in challenging sequences. The algorithm is implemented on a computer with a graphics processing unit using parallel programming.