Chen, LongTang, WenWan, Tao R.John, Nigel W.2020-01-302020-01-30Chen, L., Tang, W., Wan, T. R., & John, N. W. (2019). Self-supervised monocular image depth learning and confidence estimation. Neurocomputing, 381, 272-281. https://doi.org/10.1016/j.neucom.2019.11.0380925-231210.1016/j.neucom.2019.11.038http://hdl.handle.net/10034/623135We present a novel self-supervised framework for monocular image depth learning and confidence estimation. Our framework reduces the amount of ground truth annotation data required for training Convolutional Neural Networks (CNNs), which is often a challenging problem for the fast deployment of CNNs in many computer vision tasks. Our DepthNet adopts a novel fully differential patch-based cost function through the Zero-Mean Normalized Cross Correlation (ZNCC) to take multi-scale patches as matching and learning strategies. This approach greatly increases the accuracy and robustness of the depth learning. Whilst the proposed patch-based cost function naturally provides a 0-to-1 confidence, it is then used to self-supervise the training of a parallel network for confidence map learning and estimation by exploiting the fact that ZNCC is a normalized measure of similarity which can be approximated as the confidence of the depth estimation. Therefore, the proposed corresponding confidence map learning and estimation operate in a self-supervised manner and is a parallel network to the DepthNet. Evaluation on the KITTI depth prediction evaluation dataset and Make3D dataset show that our method outperforms the state-of-the-art results.https://creativecommons.org/licenses/by-nc-nd/4.0/Monocular depth estimationDeep convolutional neural networksConfidence mapArticle1872-8286Neurocomputing