lec15 - Announcements Today Photometric Stereo, next...

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1 CS152, Spring 2011 Intro Computer Vision Photometric Stereo Introduction to Computer Vision CSE152 Lecture 16 CS152, Spring 2011 Intro Computer Vision Announcements • Today Photometric Stereo, next lecture return to stereo CS152, Spring 2011 Intro Computer Vision Shading reveals 3-D surface geometry CS152, Spring 2011 Intro Computer Vision Two shape-from-X methods that use shading Shape-from-shading: Use just one image to recover shape. Requires knowledge of light source direction and BRDF everywhere. Too restrictive to be useful. Photometric stereo: Single viewpoint, multiple images under different lighting. 1. Arbitrary known BRDF 2. Lambertian BRDF, known lighting 3. Lambertian BRDF, unknown lighting. CS152, Spring 2011 Intro Computer Vision Photometric Stereo Rigs: One viewpoint, changing lighting CS152, Spring 2011 Intro Computer Vision Multi-view stereo vs. Photometric Stereo: Assumptions • Multi-view (binocular) Stereo – Multiple images – Static scene – Multiple viewpoints – Fixed lighting • Photometric Stereo – Multiple images – Static scene – Fixed viewpoint – Multiple lighting conditions
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2 CS152, Spring 2011 Intro Computer Vision An example of photometric stereo CS152, Spring 2011 Intro Computer Vision BRDF Bi-directional Reflectance Distribution Function ρ ( θ in , φ in ; θ out , φ out ) Function of – Incoming light direction: θ in , φ in – Outgoing light direction: θ out , φ out Ratio of incident irradiance to emitted radiance ^ n ( θ in , φ in ) ( θ out , φ out ) CS152, Spring 2011 Intro Computer Vision CS152, Spring 2011 Intro Computer Vision Photometric Stereo: Three problems 1. General but known reflectance function 2. Lambertian surfaces with known lighting 3. Lambertian surfaces with unknown lighting CS152, Spring 2011 Intro Computer Vision Photometric Stereo: General BRDF and Reflectance Map CS152, Spring 2011 Intro Computer Vision Coordinate system x y f(x,y) Surface: s (x,y) =(x,y, f(x,y)) Tangent vectors: Normal vector n = s x × s y = f x , f y , 1 Λ Ν Μ Ξ Π Ο
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3 CS152, Spring 2011 Intro Computer Vision Gradient Space (p,q) x y f(x,y) Normal vector Gradient Space : (p,q) n CS152, Spring 2011 Intro Computer Vision Image Formation For a given point A on the surface, the image irradiance E(x,y) is a function of 1. The BRDF at A 2. The surface normal at A 3. The direction of the light source n s . E(x,y)
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This note was uploaded on 08/05/2011 for the course CSE 152 taught by Professor Staff during the Spring '08 term at UCSD.

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lec15 - Announcements Today Photometric Stereo, next...

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