Extended Data Fig. 3: HADAR TeX vision demonstrated in real-world experiments (Scene-11 of the HADAR database) overcomes the ghosting effect in traditional thermal vision and sees through the darkness as if it were day.

Here TeX vision was generated by both TeX-Net and TeX-SGD for comparison. We used a semantic library instead of the exact material library for the TeX vision; see section SV.C of the Supplementary Information for more details. The semantic library consists of tree (brown), vegetation (green), soil (yellow), water (blue), metal (purple) and concrete (chartreuse). Water gives mirror images of trees and part of the sky beyond the view. Most of the water pixels can be correctly estimated as ‘water’, except for a small portion corresponding to sky image that has been estimated as ‘metal’, as metal also reflects the sky signal. TeX-Net uses both spatial information and spectral information for TeX decomposition and hence its TeX vision is spatially smoother. By contrast, TeX-SGD mainly makes use of spectral information and decomposes TeX pixel per pixel. Compared with TeX-Net, we observed that TeX-SGD is better at material identification and texture recovery for fine structures, such as the fence of the bridge, bark wrinkles and culverts. Note that the current TeX-Net was trained partially with TeX-SGD outputs. The above observations are not used to claim performance ranking between TeX-SGD and TeX-Net. Both TeX-Net and TeX-SGD confirm that HADAR TeX vision has achieved a semantic understanding of the night scene with enhanced textures comparable with RGB vision in daylight.