Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and, as recently proved, can be used for several purposes. This paper aims at increasing, by means of a deep network, the effectiveness of state-of-the-art confidence measures exploiting the local consistency assumption. We exhaustively evaluated our proposal on 23 confidence measures, including 5 top-performing ones based on random-forests and CNNs, training our networks with two popular stereo algorithms and a small subset (25 out of 194 frames) of the KITTI 2012 dataset. Experimental results show that our approach dramatically increases the effectiveness of all the 23 confidence measures on the remaining frames. Moreover, without re-training, we report a further cross-evaluation on KITTI 2015 and Middlebury 2014 confirming that our proposal provides remarkable improvements for each confidence measure even when dealing with significantly different input data. To the best of our knowledge, this is the first method to move beyond conventional pixel-wise confidence estimation.

Poggi, M., Mattoccia, S. (2017). Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps. IEEE [10.1109/CVPR.2017.483].

Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps

Poggi, Matteo
Membro del Collaboration Group
;
Mattoccia, Stefano
Membro del Collaboration Group
2017

Abstract

Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and, as recently proved, can be used for several purposes. This paper aims at increasing, by means of a deep network, the effectiveness of state-of-the-art confidence measures exploiting the local consistency assumption. We exhaustively evaluated our proposal on 23 confidence measures, including 5 top-performing ones based on random-forests and CNNs, training our networks with two popular stereo algorithms and a small subset (25 out of 194 frames) of the KITTI 2012 dataset. Experimental results show that our approach dramatically increases the effectiveness of all the 23 confidence measures on the remaining frames. Moreover, without re-training, we report a further cross-evaluation on KITTI 2015 and Middlebury 2014 confirming that our proposal provides remarkable improvements for each confidence measure even when dealing with significantly different input data. To the best of our knowledge, this is the first method to move beyond conventional pixel-wise confidence estimation.
2017
Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
4541
4550
Poggi, M., Mattoccia, S. (2017). Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps. IEEE [10.1109/CVPR.2017.483].
Poggi, Matteo; Mattoccia, Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/619385
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