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dc.contributor.authorXi, L.
dc.contributor.authorZhao, Y.
dc.contributor.authorChen, L.
dc.contributor.authorGao, Q.H.
dc.contributor.authorTang, W.
dc.contributor.authorWan, Tao Ruan
dc.contributor.authorXue, T.
dc.date.accessioned2022-03-26T13:39:32Z
dc.date.accessioned2022-05-12T08:49:43Z
dc.date.available2022-03-26T13:39:32Z
dc.date.available2022-05-12T08:49:43Z
dc.date.issued2021-06
dc.identifier.citationXi L, Zhao Y, Chen L et al (2021) Recovering dense 3D point clouds from single endoscopic image. Computer Methods and Programs in Biomedicine. 205: 106077.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18961
dc.descriptionYesen_US
dc.description.abstractRecovering high-quality 3D point clouds from monocular endoscopic images is a challenging task. This paper proposes a novel deep learning-based computational framework for 3D point cloud reconstruction from single monocular endoscopic images. An unsupervised mono-depth learning network is used to generate depth information from monocular images. Given a single mono endoscopic image, the network is capable of depicting a depth map. The depth map is then used to recover a dense 3D point cloud. A generative Endo-AE network based on an auto-encoder is trained to repair defects of the dense point cloud by generating the best representation from the incomplete data. The performance of the proposed framework is evaluated against state-of-the-art learning-based methods. The results are also compared with non-learning based stereo 3D reconstruction algorithms. Our proposed methods outperform both the state-of-the-art learning-based and non-learning based methods for 3D point cloud reconstruction. The Endo-AE model for point cloud completion can generate high-quality, dense 3D endoscopic point clouds from incomplete point clouds with holes. Our framework is able to recover complete 3D point clouds with the missing rate of information up to 60%. Five large medical in-vivo databases of 3D point clouds of real endoscopic scenes have been generated and two synthetic 3D medical datasets are created. We have made these datasets publicly available for researchers free of charge. The proposed computational framework can produce high-quality and dense 3D point clouds from single mono-endoscopy images for augmented reality, virtual reality and other computer-mediated medical applications.en_US
dc.language.isoenen_US
dc.rights© 2021 Published by Elsevier B.V. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en_US
dc.subject3D point cloudsen_US
dc.subjectMonocular endoscopic scenesen_US
dc.subjectArtificial intelligence/deep learningen_US
dc.subjectAugmented realityen_US
dc.subjectVirtual realityen_US
dc.subjectMinimally invasive surgeryen_US
dc.titleRecovering dense 3D point clouds from single endoscopic imageen_US
dc.status.refereedYesen_US
dc.date.Accepted2021-03-23
dc.date.application2021-04-03
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.identifier.doihttps://doi.org/10.1016/j.cmpb.2021.106077
dc.rights.licenseCC-BY-NC-NDen_US
dc.date.updated2022-03-26T13:39:34Z
refterms.dateFOA2022-05-12T08:50:11Z
dc.openaccess.statusopenAccessen_US


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