  Since camera modules become more and more affordable, multispectral camera
arrays have found their way from special applications to the mass market, e.g.,
in automotive systems, smartphones, or drones. Due to multiple modalities, the
registration of different viewpoints and the required cross-spectral disparity
estimation is up to the present extremely challenging. To overcome this
problem, we introduce a novel spectral image synthesis in combination with a
color agnostic transform. Thus, any recently published stereo matching network
can be turned to a cross-spectral disparity estimator. Our novel algorithm
requires only RGB stereo data to train a cross-spectral disparity estimator and
a generalization from artificial training data to camera-captured images is
obtained. The theoretical examination of the novel color agnostic method is
completed by an extensive evaluation compared to state of the art including
self-recorded multispectral data and a reference implementation. The novel
color agnostic disparity estimation improves cross-spectral as well as
conventional color stereo matching by reducing the average end-point error by
41% for cross-spectral and by 22% for mono-modal content, respectively.
