Even More Confident Predictions With Deep Machine-Learning

Matteo Poggi, Fabio Tosi, Stefano Mattoccia; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 76-84

Confidence measures aim at discriminating unreliable disparities inferred by a stereo vision system from reliable ones. A common and effective strategy adopted by most top-performing approaches consists in combining multiple confidence measures by means of an appropriately trained random-forest classifier. In this paper, we propose a novel approach by training an n-channel convolutional neural network on a set of feature maps, each one encoding the outcome of a single confidence measure. This strategy enables to move the confidence prediction problem from the conventional 1D feature maps domain, adopted by approaches based on random-forests, to a more distinctive 3D domain, going beyond single pixel analysis. This fact, coupled with a deep network appropriately trained on a small subset of images, enables to outperform top-performing approaches based on random-forests.