David C. Schneider, Markus Kettern, Anna Hilsmann, and Peter Eisert
The paper presents an approach for reconstructing head-and-shoulder portraits of people from calibrated stereo images with a high level of geometric detail. In contrast to many existing systems, our reconstructions cover the full head, including hair. This is achieved using a global intensity-based optimization approach which is stated as a parametric warp estimation problem and solved in a robust Gauss-Newton framework. We formulate a computationally efficient warp function for mesh-based estimation of depth which is based on a well known image-registration approach and adapted to the problem of 3D reconstruction. We address the use of sparse correspondence estimates for initializing the optimization as well as a coarse-to-fine scheme for reconstructing without specific initialization. We discuss issues of regularization and brightness constancy violations and show various results to demonstrate the effectiveness of the approach.
Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation