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Obtaining models of dynamic 3D objects is an important part of content generation for computer graphics. Numerous methods have been extended from static scenarios to model dynamic scenes. If the states or poses of the dynamic object repeat often during a sequence (but not necessarily periodically), the authors call such a repetitive motion. There are many objects, such as toys, machines, and humans, undergoing repetitive motions. The key observation is that when a motion state repeats, they can sample the scene under the same motion state again but using a different set of parameters; thus providing more information of each motion state. This enables robustly acquiring dense 3D information difficult for objects with repetitive motions using only simple hardware.
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