Enhanced Depth Estimation using a Combination of Structured Light Sensing and Stereo Reconstruction.

We present a novel approach for depth sensing that combines structured light scanning and stereo reconstruc- tion. High-resolution disparity maps are derived in an iterative upsampling process that jointly optimizes measurements from graph cuts-based stereo reconstruction and structured light sensing using an accelerated a-expansion algorithm. Different from previously proposed fusion approaches, the disparity estimation is initialized using the low-resolution structured light prior. This results in a dense disparity map that can be computed very efficiently and which serves as an improved prior for subsequent iterations at higher resolu- tions. The advantages of the proposed fusion approach over the sole use of stereo are threefold. First, for pixels that exhibit prior knowledge from structured lighting, a reduction of the disparity search range to the uncertainty interval of the prior allows for a significant reduction of ambiguities. Second, the resulting limited search range greatly reduces the runtime of the algorithm. Third, the structured light prior enables a dynamic tuning of the smoothness constraint to allow for a better depth estimation for inclined surfaces. This paper has been accepted for presentation and inclusion into the proceedings of VISAPP 2016 - International Conference on Computer Vision Theory and Applications (visapp.visigrapp.org).