We present a system for photo-realistic 3D model acquisition from the combination of range and image sensing. The input is a sequence of unregistered range scans of the scene and a sequence of unregistered 2-D photographs of the same scene. The output is a geometrically correct texture-mapped model of the scene. Segmentation algorithms reduce the complexity of the dense range data-sets and provide stable features of interest which can be used for registration purposes. Solid modeling provides geometrically correct 3-D models, which can be automatically texture mapped from current or historic photographs. The system is comprehensive in that it addresses all phases of the modeling problem with a particular emphasis on automating the entire process. We present results from scanning and modeling a variety of objects ranging from buildings in New York City to the Cathedral of Ste. Pierre in Beauvais, France. The system currently resides on a mobile robot which will be used to model archaeological sites in Egypt.
Peter Allen is professor of Computer Science at Columbia University. He received the A.B. degree from Brown University in Mathematics-Economics, the M.S. in Computer Science from the University of Oregon and the Ph.D. in Computer Science from the University of Pennsylvania. His current research interests include real-time computer vision, dextrous robotic hands, 3-D modeling and sensor planning. In recognition of his work, Professor Allen has been named a Presidential Young Investigator by the National Science Foundation.