  Multispectral stereoscopy is an emerging field. A lot of work has been done
in classical stereoscopy, but multispectral stereoscopy is not studied as
frequently. This type of stereoscopy can be used in autonomous vehicles to
complete the information given by RGB cameras. It helps to identify objects in
the surroundings when the conditions are more difficult, such as in night
scenes. This paper focuses on the RGB-LWIR spectrum. RGB-LWIR stereoscopy has
the same challenges as classical stereoscopy, that is occlusions, textureless
surfaces and repetitive patterns, plus specific ones related to the different
modalities. Finding matches between two spectrums adds another layer of
complexity. Color, texture and shapes are more likely to vary from a spectrum
to another. To address this additional challenge, this paper focuses on
estimating the disparity of people present in a scene. Given the fact that
people's shape is captured in both RGB and LWIR, we propose a novel method that
uses segmentation masks of the human in both spectrum and than concatenate them
to the original images before the first layer of a Siamese Network. This method
helps to improve the accuracy, particularly within the one pixel error range.
