Chen, L., Tang, W., Wan, T.R. and John, N.W., 2020. Self-supervised monocular image depth learning and confidence estimation. Neurocomputing, 381 (March), 272-281.
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DOI: 10.1016/j.neucom.2019.11.038
Abstract
We 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 normalised 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.
Item Type: | Article |
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ISSN: | 0925-2312 |
Uncontrolled Keywords: | monocular depth estimation; deep convolutional neural networks; confidence map |
Group: | Faculty of Science & Technology |
ID Code: | 33253 |
Deposited By: | Symplectic RT2 |
Deposited On: | 17 Jan 2020 15:23 |
Last Modified: | 14 Mar 2022 14:19 |
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