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SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery.

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Randomly connected neural networks for self-supervised monocular depth estimation., и . Comput. methods Biomech. Biomed. Eng. Imaging Vis., 10 (4): 390-399 (2022)SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery., , , , , , , , , и 17 other автор(ы). CoRR, (2023)SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery., , , , , , , , , и 16 other автор(ы). Medical Image Anal., (января 2024)Analysing deep reinforcement learning agents trained with domain randomisation., , , , , и . Neurocomputing, (2022)Stereo Depth Estimation via Self-supervised Contrastive Representation Learning., и . MICCAI (8), том 13437 из Lecture Notes in Computer Science, стр. 604-614. Springer, (2022)Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation., , , , и . CoRR, (2019)VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery., , , , и . Comput. methods Biomech. Biomed. Eng. Imaging Vis., 9 (4): 331-338 (2021)See-Through Vision With Unsupervised Scene Occlusion Reconstruction., , и . IEEE Trans. Pattern Anal. Mach. Intell., 44 (7): 3779-3790 (2022)Improving Visual Representation Learning through Perceptual Understanding., , и . CoRR, (2022)Improving Visual Representation Learning Through Perceptual Understanding., , и . CVPR, стр. 14486-14495. IEEE, (2023)