ACM SIGGRAPH 2007 papers
Webcam clip art: appearance and illuminant transfer from time-lapse sequences
ACM SIGGRAPH Asia 2009 papers
The global network of outdoor webcams: properties and applications
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
What Do the Sun and the Sky Tell Us About the Camera?
International Journal of Computer Vision
Shape-based image segmentation through photometric stereo
Computer Vision and Image Understanding
Learning of optimal illumination for material classification
Proceedings of the 32nd DAGM conference on Pattern recognition
The scale of geometric texture
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Heliometric stereo: shape from sun position
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Removing the example from example-based photometric stereo
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
International Journal of Computer Vision
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We propose a new approach called "appearance clustering" for scene analysis. The key idea in 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. We achieve this by analyzing an image sequence of a scene as it is illuminated by a smoothly moving distant source. Each pixel thus gives rise to a "continuous appearance profile" that yields information about derivatives of the BRDF w.r.t source direction. This information is directly related to the surface normal of the scene point when the source path follows an unstructured trajectory (obtained, say, by "hand-waving"). Based on this observation, we transform the appearance profiles and propose a metric that can be used with any unsupervised clustering algorithm to obtain iso-normal clusters. We successfully demonstrate appearance clustering for complex indoor and outdoor scenes. In addition, iso-normal clusters serve as excellent priors for scene geometry and can strongly impact any vision algorithm that attempts to estimate material, geometry and/or lighting properties in a scene from images. We demonstrate this impact for applications such as diffuse and specular separation, both calibrated and uncalibrated photometric stereo of non-lambertian scenes, light source estimation and texture transfer.