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EndoMapper: Real-time mapping from endoscopic video

Deliverables

(Nr)SfM for labeling matches in real endoscopies (Report)

(Nr)SfM-based learning of Siamese CNNs for Correspondence, report

Labelling deformation in endoscopies (Report)

Simulated data generation and augmentation framework, report.

Isometric NRSfM for the tubular topology (Report)

Articles for isometric NRSfM, taking point correspondences as inputs.

Def SLAM (Report)

The different articles assembled in the report will describe the basis for doing Deformable Visual mapping.

Website and Logo

Designing the logo. Create the EndoMapper website in the .eu domain

Searching for OpenAIRE data...

Publications

SD-DefSLAM: Semi-Direct Monocular SLAM for Deformable and Intracorporeal Scenes

Author(s): Juan J. Gómez Rodríguez, José Lamarca, Javier Morlana, Juan D. Tardós, José M. M. Montiel
Published in: arXiv e-prints (submitted to IEEE Int. Conference on Robotics and Automation), 2020

Structure-from-motion analysis may generate an accurate automated bowel preparation score.

Author(s): Chadebecq F., Mountney P., Ahmad O. F., Kader R., Lovat L. B., Stoyanov D.
Published in: UEG Journal. Abstract Issue, 2020, Page(s) 765

Extracción de características en imágenes de procedimientos médicos con técnicas de deep learning (Feature extraction on medical images with Deep Learning)

Author(s): Oscar Leon Barbed
Published in: 2020

Estimación de profundidad con redes neuronales profundas en vídeos de endoscopias (Depth estimation with deep neural networks in endoscopy videos)

Author(s): David Recasens
Published in: 2020

DefSLAM: Tracking and Mapping of Deforming Scenes From Monocular Sequences

Author(s): Jose Lamarca, Shaifali Parashar, Adrien Bartoli, J. M. M. Montiel
Published in: IEEE Transactions on Robotics, 2020, Page(s) 1-13, ISSN 1552-3098
DOI: 10.1109/tro.2020.3020739