Learning Depth from Endoscopic Images

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We propose an unsupervised approach to predict depth maps from images captured by a wireless endoscopic capsule. Recent advances in deep learning have shown that accurate depth maps can be predicted from a single image, where the deep network is trained via unsupervised or self-supervised learning by using monocular video sequences or stereo image pairs. However, directly applying these techniques to endoscopic images does not yield satisfactory results owing to the inherent difficulties of the wireless capsule imaging such as dim lighting and low-resolution of images, which are different from normal imaging conditions. For that reason, we exploit the environmental characteristics of endoscopic images-there is no external light source except ones attached to the capsule. Based on this condition, we propose the direct attenuation model-based depth map prediction scheme to guide depth prediction and to add meaningful cues to the loss function. We experimentally verify the proposed method with various endoscopic images.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2019-09-17
Language
English
Citation

7th International Conference on 3D Vision, 3DV 2019, pp.126 - 134

DOI
10.1109/3DV.2019.00023
URI
http://hdl.handle.net/10203/271082
Appears in Collection
ME-Conference Papers(학술회의논문)
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