(e.g., low-light and saturated regions), by adding structure-preservation and Translation, for light effects (e.g., glow/glare) and uninformative regions We handle the fake depth problem, which occurs due to the unsupervised/unpaired Stereo images, and simultaneously also train the day/night translation network. Images using the available disparity supervision from the corresponding day We then train a stereo network on the rendered night stereo Translation network that can render realistic night stereo images from day In training the network, our method does not require ground-truthĭisparities of the night images, or paired day/night images.

ToĪddress the problem, we introduce a network joining day/night translation and Glare/glow, and have sufficiently far depth ranges is extremely intractable. To obtain proper disparity ground-truths that are dense, independent from Train a network on night stereo images in a fully supervised manner. Is not only about low-light and dense noise, but also about glow/glare, flares, Tan Download PDF Abstract: Nighttime stereo depth estimation is still challenging, as assumptionsĪssociated with daytime lighting conditions do not hold any longer. Authors: Aashish Sharma, Lionel Heng, Loong-Fah Cheong, Robby T.

Joint stereo or stereo