Clustering Appearance for Scene Analysis

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 clust...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Sanjeev J. Koppal, Srinivasa G. Narasimhan
• Keywords: Computer Vision, CVPR 2006, Geometry And/or Lighting, Scene Analysis, Scene Geometry, Scene Points, Scene Structure