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Issue No.08 - August (2009 vol.31)
pp: 1375-1385
Sanjeev J. Koppal , Carnegie Mellon University, Pittsburgh
Srinivasa G. Narasimhan , Carnegie Mellon Univeristy, Pittsburgh
A new technique is proposed for scene analysis, called "appearance clustering.” The key result of this approach is that the scene points can be clustered according to their surface normals, even when the geometry, material, and lighting are all unknown. This is achieved by analyzing an image sequence of a scene as it is illuminated by a smoothly moving distant light source. In such a scenario, the brightness measurements at each pixel form a "continuous appearance profile.” When the source path follows an unstructured trajectory (obtained, say, by smoothly hand-waving a light source), the locations of the extrema of the appearance profile provide a strong cue for the scene point's surface normal. Based on this observation, a simple transformation of the appearance profiles and a distance metric are introduced that, together, can be used with any unsupervised clustering algorithm to obtain isonormal clusters of a scene. We support our algorithm empirically with comprehensive simulations of the Torrance-Sparrow and Oren-Nayar analytic BRDFs, as well as experiments with 25 materials obtained from the MERL database of measured BRDFs. The method is also demonstrated on 45 examples from the CURET database, obtaining clusters on scenes with real textures such as artificial grass and ceramic tile, as well as anisotropic materials such as satin and velvet. The results of applying our algorithm to indoor and outdoor scenes containing a variety of complex geometry and materials are shown. As an example application, isonormal clusters are used for lighting-consistent texture transfer. Our algorithm is simple and does not require any complex lighting setup for data collection.
Appearance modeling, physics-based vision, scene reconstruction, active illumination, material invariants, relighting.
Sanjeev J. Koppal, Srinivasa G. Narasimhan, "Appearance Derivatives for Isonormal Clustering of Scenes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 8, pp. 1375-1385, August 2009, doi:10.1109/TPAMI.2008.148
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