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.

Website: http://www.cs.columbia.edu/robotics

Biosketch:
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.