Self-Supervised Monocular Image Depth Learning and Confidence Estimation

Long Chen; Wen Tang; Nigel John

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arXiv · 1803.05530

Self-Supervised Monocular Image Depth Learning and Confidence Estimation

Long Chen·Wen Tang·Nigel John

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Abstract

Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks. We propose a novel framework for depth estimation from monocular images with corresponding confidence in a self-supervised manner. A fully differential patch-based cost function is proposed by using the Zero-Mean Normalized Cross Correlation (ZNCC) that takes multi-scale patches as a matching strategy. This approach greatly increases the accuracy and robustness of the depth learning. In addition, the proposed patch-based cost function can provide a 0 to 1 confidence, which is then used to supervise the training of a parallel network for confidence map learning and estimation. Evaluation on KITTI dataset shows that our method outperforms the state-of-the-art results.

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BibTeX RIS

Long Chen, Wen Tang, Nigel John. 2018-03-14. Self-Supervised Monocular Image Depth Learning and Confidence Estimation. https://arxiv.org/abs/1803.05530

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