Title | ||
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Enhanced Depth Estimation using a Combination of Structured Light Sensing and Stereo Reconstruction. |
Abstract | ||
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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). |
Year | Venue | Field |
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2016 | VISIGRAPP (3: VISAPP) | Cut,Computer vision,Structured light,Pattern recognition,Computer science,Stereo reconstruction,Artificial intelligence,Pixel,Upsampling,Smoothness,Computer stereo vision,Structured-light 3D scanner |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
14 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Andreas Wittmann | 1 | 0 | 0.34 |
Anas Al-Nuaimi | 2 | 79 | 6.86 |
Eckehard G. Steinbach | 3 | 2221 | 299.71 |
Georg Schroth | 4 | 250 | 12.71 |