A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation
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The recent advance of monocular depth estimation is largely based on deeply nested convolutional networks, combined with supervised training. However, it still remains arduous to collect large-scale ground truth depth (or disparity) maps for supervising the networks. This paper presents a simple yet effective semi-supervised approach for monocular depth estimation. Inspired by the human visual system, we propose a student-teacher strategy in which a shallow student network is trained with the auxiliary information obtained from a deeper and accurate teacher network. Specifically, we first train the stereo teacher network fully utilizing the binocular perception of 3D geometry, and then use depth predictions of the teacher network for supervising the student network for monocular depth inference. This enables us to exploit all available depth data from massive unlabeled stereo pairs that are relatively easier-to-obtain. We further introduce a data ensemble strategy that merges multiple depth predictions of the teacher network to improve the training samples for the student network. Additionally, stereo confidence maps are provided to avoid inaccurate depth estimates being used when supervising the student network. Our new training data, consisting of 1 million outdoor stereo images taken using hand-held stereo cameras, is hosted at the project webpage. Lastly, we demonstrate that the monocular depth estimation network provides feature representations that are suitable for some high-level vision tasks such as semantic segmentation and road detection. Extensive experiments demonstrate the effectiveness and flexibility of the proposed method in various outdoor scenarios.
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