Guided Robust Matte-Model Fitting for Accelerating Multi-light Reflectance

The generation of a basic matte model is at the core of many multi-light reflectance processing approaches, such as Photometric Stereo or Reflectance Transformation Imag- ing. To recover information on objects’ shape and appearance, the matte model is used directly or combined with specialized methods for modeling high-frequency behaviors. Multivariate robust regression offers a general solution to reliably extract the matte com- ponent when source data is heavily contaminated by shadows, inter-reflections, specular- ity, or noise. However, robust multivariate modeling is usually very slow. In this paper, we accelerate robust fitting by drastically reducing the number of tested candidate solu- tions using a guided approach. Our method propagates already known solutions to nearby pixels using a similarity-driven flood-fill strategy, and exploits this knowledge to order possible candidate solutions and to determine convergence conditions. The method has been tested on objects with a variety of reflectance behaviors, showing state-of-the-art accuracy with respect to current solutions, and a significant speed-up without accuracy reduction with respect to multivariate robust regression.