Shape from Autostereograms

We study the problem of shape reconstruction from stereo images based on a weighted area minimization process of a depth function. As a simple example we present an eecient shape reconstruction from computer generated autostereograms. A minimal surface area based correlation is applied to accurately reconstruct the surface structure embedded rst in one autostereogram image, and next in two or morèstereo' images. The minimal area approach proved itself as a useful geometric measure in recent reconstruction and enhancement applications in computer vision and image processing. Here we develop a simpliied version for the Faugeras and Keriven 5] stereo reconstruction model, and apply a weighted area measure as part of a solution to the correspondence extraction in the shape from stereo and the shape from autostereogram problems. The proposed schemes are computationally eecient and yield accurate 3D reconstructions for smooth as well as non-smooth surfaces.