Reconstruction of dynamic articulated three-dimensional models from range scans

Our vision is to enable efficient acquisition and synthesis of highly detailed 3D surface models that are also easy to animate in a plausible and realistic way. The state-of-the-art surface acquisition technology is range scanning, which can measure surface geometry with a high degree of accuracy and speed. However, the output is only a partial view of the surface that has much missing data, and there is no tracking of the surface motion in the case of a moving subject. To reconstruct a complete model of the subject, we must align multiple range scans taken from different times and viewpoints to fill in the missing data and track the motion of the surface. At the same time, we would like to fit a reduced deformable model that expresses the surface motion in terms of a few intuitive parameters.;In this dissertation, we develop algorithms to process and align multiple range scans of a moving articulated subject. Our algorithms can automatically align multiple scans to a common pose, thus reconstructing the full geometry of an articulated subject along with a model of its motion. Our methods perform this alignment in a completely unsupervised way: without markers, a template, or a user-defined segmentation of the surface. A key contribution is the use of discrete optimization techniques to automatically estimate the articulated structure of the surface based on its motion.