High-resolution depth for binocular image-based modeling

We propose 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 on 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 propose a mesh-based data-term for large images, and a smoothness term using robust error norms to allow detailed surface geometry. We show that 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, we argue that our approach is well suited for a semi-interactive reconstruction work-flow, for which we propose several tools.