Integrating Stereo with Shape-from-Shading derived Orientation Information

Binocular stereo has been extensively studied for extracting the shape of a scene. The challenge is in matching features between two images of a scene; this is the correspondence problem. Shape from shading (SfS) is another method of extracting shape. This models the interaction of light with the scene surface(s) for a single image. These two methods are very different; stereo uses surface features to deliver a depth-map, SfS uses shading, albedo and lighting information to infer the differential of the depth-map. In this paper we develop a framework for the integration of both depth and orientation information. Dedicated algorithms are used for initial estimates. A Gaussian-Markov random field then represents the depth-map, Gaussian belief propagation is used to approximate the MAP estimate of the depth-map. Integrating information from both stereo correspondences and surface normals allows fine surface details to be estimated

Integrating Stereo with Shape-from-Shading derived Orientation Information

Binocular stereo has been extensively studied for extracting the shape of a scene. The challenge is in matching features between two images of a scene; this is the correspondence problem. Shape from shading (SfS) is another method of extracting shape. This models the interaction of light with the scene surface(s) for a single image. These two methods are very different; stereo uses surface features to deliver a depth-map, SfS uses shading, albedo and lighting information to infer the differential of the depth-map. In this paper we develop a framework for the integration of both depth and orientation information. Dedicated algorithms are used for initial estimates. A Gaussian-Markov random field then represents the depth-map, Gaussian belief propagation is used to approximate the MAP estimate of the depth-map. Integrating information from both stereo correspondences and surface normals allows fine surface details to be estimated