Stereo matching based on classification of materials

Abstract

Stereo matching is one of the most important and fundamental topics in computer vision. Encouraging self-similar pixels to be assigned to the same label has been proved to be effective for stereo. A typical way of taking advantage of self-similarity is performing a color segmentation on the image and motivating the pixels within each segment to share an identical label. However, some cases cannot be handled by image segmentation, such as the pixels in disconnected regions. This paper proposes a stereo method based on the assumption, that a 3D scene is a collection of a few smooth surfaces and a few classes of reflective materials, such that the 3D points belonging to an identical material are likely to lie on a small number of surfaces and the 3D points lying on a single surface belong to a few classes of reflective materials. Each material is expected to have specific albedo properties. This paper presents two methods for classifying the albedo properties depending on whether the illumination environment is known, without recovering the albedo parameters. The proposed model is formulated as an energy function incorporating some new priors, that is optimized via fusion move algorithm.