High-resolution depth for binocular image-based modeling

• A binocular stereo method optimized for high resolutions images is proposed.
• The approach yields surface detail from 2 images comparable to multiview methods.
• Formulated as Gauss–Newton optimization, it is mathematically straightforward.
• Interactive tools influencing the reconstruction process are discussed.

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.

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