Deep Learning for Confidence Information in Stereo and ToF Data Fusion

Gianluca Agresti, Ludovico Minto, Giulio Marin, Pietro Zanuttigh; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 697-705

This paper proposes a novel framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and a stereo vision system. The key problem of balancing between the two sources of information is solved by extracting confidence maps for both sources using deep learning. We introduce a novel synthetic dataset accurately representing the data acquired by the proposed setup and use it to train a Convolutional Neural Network architecture. The machine learning framework estimates the reliability of both data sources at each pixel location. The two depth fields are finally fused enforcing the local consistency of depth data taking into account the confidence information. Experimental results show that the proposed approach increases the accuracy of the depth estimation.