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ZENODO
Dataset . 2023
Data sources: Datacite
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ZENODO
Dataset . 2023
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
Data sources: ZENODO
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StereoMIS

Authors: Hayoz Michel; Allan Max;
Abstract

StereoMIS StereoMIS is a dataset for Simultaneous Localization and Mapping (SLAM) in endoscopic surgery. The dataset has been recorded using the da Vinci Xi surgical robot, capturing a stereo video stream and camera forward kinematics. It consists of three in-vivo porcine subjects with 11 surgical sequences containing breathing, tool motion, and tissue deformations. Each folder contains: a video file with vertically stacked video feeds from the stereo endoscopes automatically generated tool masks for the left video frames camera forward kinematics synchronized with the video feed csv-file containing start and stop frames of the relevant sequence If you use the dataset in your research, please cite our paper: @article{hayoz2023pose, title={Learning how to robustly estimate camera pose in endoscopic videos}, author={Michel Hayoz, Christopher Hahne, Mathias Gallardo, Daniel Candinas, Thomas Kurmann, Max Allan, Raphael Sznitman}, journal={International Journal of Computer Assisted Radiology and Surgery 2023} doi={https://doi.org/10.1007/s11548-023-02919-w} } Release notes: added missing P3 added missing start and stop frames duration total trajectory length frame-rate frame-rate extracted split P1 5min 19s 1323 mm 60 fps 30 fps train P2_0 3min 28s 434 mm 52.6 fps 26.3 fps test P2_1 2min 36s 1155 mm 54.4 fps 27.2 fps test P2_2 2min 37s 736 mm 54.4 fps 27.2 fps test P2_3 2min 35s 302 mm 54.4 fps 27.2 fps test P2_4 2min 37s 1824 mm 54.4 fps 27.2 fps test P2_5 3min 18s 1671 mm 54.2 fps 27.1 fps test P2_6 52s 205 mm 26.1 fps 26.1 fps test P2_7 2min 26s 725 mm 37.0 fps 37.0 fps test P2_8 3min 55s 950 mm 39.0 fps 39.0 fps test P3 2min 58s 545 mm 60.0 fps 30.0 fps test This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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Keywords

Endoscopy, Stereo, da Vinci, deformable SLAM

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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