Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge. Through FetReg2021 challenge, we released the first large-scale multi-centre dataset of fetoscopy laser photocoagulation procedure. The dataset contains 2,718 pixel-wise annotated images (for background, vessel, fetus, tool classes) from 24 different in vivo TTTS fetoscopic surgeries and 24 unannotated video clips video clips containing 9,616 frames for training and testing. The dataset is useful for the development of generalized and robust semantic segmentation and video mosaicking algorithms for long duration fetoscopy videos.
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This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divi
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