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.
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