Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images
Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery. Recent work has shown that depth estimation from a stereo images pair could be solved with convolutional neural networks. However, most recent depth estimation models were trained on datasets with per-pixel ground truth. Such data is esp...
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Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images
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TN_cdi_proquest_journals_2550958714
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2550958714
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2331-8422
