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[Dissertation Talk] Shapes, Paint, and Light
Seminar: Departmental | May 1 | 12-1 p.m. | Sutardja Dai Hall, 254 SDH
Jonathan Barron, UC Berkeley
A core problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to over-constrain the problem. Recovering these same properties from a single image seems almost impossible in comparison --- the space of shapes, paint, and lights that exactly reproduce a single image is vast.
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