Predicting Disparity Distributions
Gustav Häger Mikael Persson Michael Felsberg Gustav Häger Mikael Persson Michael Felsberg
We investigate a novel deep-learning-based approach to estimate uncertainty in stereo disparity prediction networks. Current state-of-the-art methods often formulate disparity prediction as a regression problem with a single scalar output in each pixel. This can be problematic in practical applications as in many cases there might not exist a single well defined disparity, for example in cases of ...