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Self-supervised monocular image depth learning and confidence estimation
Chen, Long Tang, Wen Wan, Tao R. John, Nigel W.
Chen, Long
Tang, Wen
Wan, Tao R.
John, Nigel W.
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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 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.
Citation
Chen, 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.2019.11.038
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Elsevier
Journal
Neurocomputing
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DOI
10.1016/j.neucom.2019.11.038
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0925-2312
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1872-8286