AbstractThe first strategy, known as passive vision, attempts to analyze the structure of the scene under ambient light. In contrast, the second, known as active vision, attempts to reduce the way in which images are formed. Sensors that capitalize on active vision can resolve most of the ambiguities found with 2-D imaging systems. For 3D human sensing, model based approaches are suitable since image-based methods do not produce 3D information. The 3D shape estimation done in the model-based approaches is a classic but open problem in computer vision. The development of 3D video in recent years realizes 3D surface capturing of human in motion as is. In this seminar, introduce 3D human sensing algorithms based on 3D video. Since 3D video capturing does not require the object to attach special markers, can capture the original information such as body motion or viewing directions without any disturbance caused by the sensing system itself. Visibility of the Observed Surface Next we introduce the visibility of the observed surface Mt. Since Mt is estimated from the multi-viewpoint images, the vertices on Mt can be categorized by the number of the cameras which can observe it. If one or less camera can observe a vertex v, v cannot be photo-consistent and the position of v is interpolated by its neighbors. On the other hand, if two or more cameras can observe v, v should be photo-consistent and its 3D position is estimated explicitly by the stereo-matching.
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