We developed a binocular stereo method which is optimized for reconstructing surface detail and exploits the high image resolutions of current digital cameras. Our method occupies a middle ground between stereo algorithms focused at depth layering of cluttered scenes and multi-view ”object reconstruction” approaches which require a higher view count. It is based on global non-linear optimization of continuous scene depth rather than discrete pixel disparities. We use a mesh-based data-term for large images, and a smoothness term using robust error norms to allow detailed surface geometry. The continuous optimization approach enables interesting extensions beyond the core algorithm: Firstly, with small changes to the data-term camera parameters instead of depth can be optimized in the same framework. Secondly, our approach is well suited for a semi-interactive reconstruction work-flow, for which we propose several tools.
D. Blumenthal-Barby, P. Eisert
High-Resolution Depth For Binocular Image-Based Modelling, Computers & Graphics, vol. 39, pp. 89-100, Apr. 2014.