Differential Geometric Inference in Surface Stereo

Many traditional two-view stereo algorithms explicitly or implicitly use the frontal parallel plane assumption when exploiting contextual information since, e.g., the smoothness prior biases toward constant disparity (depth) over a neighborhood. This introduces systematic errors to the matching process for slanted or curved surfaces. These errors are nonnegligible for detailed geometric modeling of natural objects such as a human face. We show how to use contextual information geometrically to avoid such errors. A differential geometric study of smooth surfaces allows contextual information to be encoded in Cartan's moving frame model over local quadratic approximations, providing a framework of geometric consistency for both depth and surface normals; the accuracy of our reconstructions argues for the sufficiency of the approximation. In effect, Cartan's model provides the additional constraint necessary to move beyond the frontal parallel plane assumption in stereo reconstruction. It also suggests how geometry can extend surfaces to account for unmatched points due to partial occlusion.