  A major focus of recent developments in stereo vision has been on how to
obtain accurate dense disparity maps in passive stereo vision. Active vision
systems enable more accurate estimations of dense disparity compared to passive
stereo. However, subpixel-accurate disparity estimation remains an open problem
that has received little attention. In this paper, we propose a new learning
strategy to train neural networks to estimate high-quality subpixel disparity
maps for semi-dense active stereo vision. The key insight is that neural
networks can double their accuracy if they are able to jointly learn how to
refine the disparity map while invalidating the pixels where there is
insufficient information to correct the disparity estimate. Our approach is
based on Bayesian modeling where validated and invalidated pixels are defined
by their stochastic properties, allowing the model to learn how to choose by
itself which pixels are worth its attention. Using active stereo datasets such
as Active-Passive SimStereo, we demonstrate that the proposed method
outperforms the current state-of-the-art active stereo models. We also
demonstrate that the proposed approach compares favorably with state-of-the-art
passive stereo models on the Middlebury dataset.
