Deep Learning Based Cross-Spectral Disparity Estimation For Stereo Imaging

Recently, cross-spectral stereo-camera setups found their way from special applications to mass market, especially in smartphones, automotive systems, or drones. In the following, a novel concept is introduced to bring stereo cameras and cross-spectral disparity estimation together. So far, either m...

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Bibliographic Details
Main Authors: Genser, Nils, Spruck, Andreas, Seiler, Jurgen, Kaup, Andre
Format: Conference Proceeding
Language:English
Subjects:
Cameras
cross-spectral imaging
disparity estimation
Estimation
Image color analysis
Machine learning
Photonics
Stereo matching
Training
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Summary:Recently, cross-spectral stereo-camera setups found their way from special applications to mass market, especially in smartphones, automotive systems, or drones. In the following, a novel concept is introduced to bring stereo cameras and cross-spectral disparity estimation together. So far, either monomodal stereo algorithms exist that are not suitable for cross-spectral image registration, or structural template matching is applied that achieves a low quality. To overcome these limitations, a technique is proposed to synthesize arbitrary spectral components from widely available color stereo databases, and to retrain mono-modal deep learning methods. In this contribution, the estimation of spectral bands based on random processes is shown together with noise models, which also allow for a robust registration of narrowband components. The theoretical examination is completed by an extensive evaluation, including a self-manufactured cross-spectral camera setup. In comparison to state-of-the-art techniques, the end-point error is on average reduced by a factor of seven.
ISSN:2381-8549
DOI:10.1109/ICIP40778.2020.9191353