title: Example-based photometric stereo: shape reconstruction with general, varying BRDFs.
abstract: This paper presents a technique for computing the geometry of objects with general reflectance properties from images. For surfaces with varying material properties, a full segmentation into different material types is also computed. It is assumed that the camera viewpoint is fixed, but the illumination varies over the input sequence. It is also assumed that one or more example objects with similar materials and known geometry are imaged under the same illumination conditions. Unlike most previous work in shape reconstruction, this technique can handle objects with arbitrary and spatially-varying BRDFs. Furthermore, the approach works for arbitrary distant and unknown lighting environments. Finally, almost no calibration is needed, making the approach exceptionally simple to apply.
authors.affiliation: Computer Science Department, University of Toronto, 10 King's College Road, Room 3302, Toronto, ON M5S 3G4 Canada. hertzman@dgp.toronto.edu
authors.forename: Aaron
authors.initials: A
authors.lastname: Hertzmann
authors.forename: Steven M
authors.initials: SM
authors.lastname: Seitz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003124:Colorimetry / Q000379:methods*; D003198:Computer Simulation; D003710:Demography; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D010780:Photogrammetry / Q000379:methods*; D010783:Photometry / Q000379:methods*