A Variational Framework for Reconstructing 3D Shape and Photometry from Multiple Images

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This project aims at designing algorithms to infer 3-D models of the geometry (shape) and photometry (material) of objects from collections of images. Such algorithms are based on a representing visual scenes as dense surfaces, defined implicitly as functions of the measured images, and entail the numerical solution of partial differential equations. Our research aims at integrating in a unified analytical framework many ``shape from X'' algorithms for reconstructing spatial properties of a scene from images, including stereo, shape from shading, and shape from motion. Applications of the technology we plan to develop ranges from geology to medicine, manufacturing, security, to entertainment.