We address the problem of scene depth recovery within cross-spectral stereo imagery (each image sensed over a differing spectral range). We compare several robust matching techniques which are able to capture local similarities between the structure of crossspectral images and a range of stereo optimisation techniques for the computation of valid depth estimates in this case. Specifically we deal with the recovery of dense depth information from thermal (far infrared spectrum) and optical

more »

... le spectrum) image pairs where large differences in the characteristics of image pairs make this task significantly more challenging than the common stereo case. We show that the use of dense gradient features, based on Histograms of Oriented Gradient (HOG) descriptors, for pixel matching in combination with a strong match optimisation approach can produce largely valid, yet coarse, dense depth estimates suitable for object localisation or environment navigation. The proposed solution is compared and shown to work favourably against prior approaches based on using Mutual Information (MI) or Local Self-Similarity (LSS) descriptors.