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Three-dimensional (3-D) video, which is a sequence of time-varying mesh models generated in a multi-camera studio, is attracting increased attention, because it can record and reproduce the 3-D information of real-world objects with high accuracy. As one of the most important preprocessings for indexing, annotation, retrieval, and many other functions in management of a 3-D video database, it is necessary to temporally segment 3-D video into meaningful and manageable segments. We have developed robust and effective segmentation algorithms using histogram-based feature vector representation, striving to understand and manage 3-D video contents. We have developed two approaches to generate feature vectors by vertex positions in the mesh models: one uses the Cartesian coordinate system and the other employs the spherical coordinate system. Then, 3-D video is segmented by the motion intensity of an object, which is analyzed by the feature vectors. The segmentation algorithms we have developed are applied to three different 3-D video sequences. A statistical method is presented to evaluate the segmentation results. High recall and precision rates of 0.95 and 0.77, respectively, are achieved in the best case.