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Title: GitHub - lppllppl920/DenseDescriptorLearning-Pytorch: Official Repo for the paper "Extremely Dense Point Correspondences using a Learned Feature Descriptor" (CVPR 2020)

Open Graph Title: GitHub - lppllppl920/DenseDescriptorLearning-Pytorch: Official Repo for the paper "Extremely Dense Point Correspondences using a Learned Feature Descriptor" (CVPR 2020)

X Title: GitHub - lppllppl920/DenseDescriptorLearning-Pytorch: Official Repo for the paper "Extremely Dense Point Correspondences using a Learned Feature Descriptor" (CVPR 2020)

Description: Official Repo for the paper "Extremely Dense Point Correspondences using a Learned Feature Descriptor" (CVPR 2020) - lppllppl920/DenseDescriptorLearning-Pytorch

Open Graph Description: Official Repo for the paper "Extremely Dense Point Correspondences using a Learned Feature Descriptor" (CVPR 2020) - lppllppl920/DenseDescriptorLearning-Pytorch

X Description: Official Repo for the paper "Extremely Dense Point Correspondences using a Learned Feature Descriptor" (CVPR 2020) - lppllppl920/DenseDescriptorLearning-Pytorch

Mail addresses
xingtongliu@jhu.edu
unberath@jhu.edu

Opengraph URL: https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch

X: @github

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https://patch-diff.githubusercontent.com/lppllppl920/DenseDescriptorLearning-Pytorch#extremely-dense-point-correspondences-using-a-learned-feature-descriptor
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Xingtong Liuhttp://www.cs.jhu.edu/~xingtongl/
Mathias Unberathhttps://www.cs.jhu.edu/faculty/mathias-unberath/
this paperhttps://ieeexplore.ieee.org/document/9157241
https://patch-diff.githubusercontent.com/lppllppl920/DenseDescriptorLearning-Pytorch#instructions
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herehttps://drive.google.com/file/d/1RwmxpI7kuZ7teB14EQY3u06CAV7-KsX0/view?usp=sharing
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SAGE: SLAM with Appearance and Geometry Prior for Endoscopy (ICRA 2022)https://github.com/lppllppl920/SAGE-SLAM
Neighborhood Normalization for Robust Geometric Feature Learning (CVPR 2021)https://github.com/lppllppl920/NeighborhoodNormalization-Pytorch
Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment (MICCAI 2020)https://github.com/lppllppl920/DenseReconstruction-Pytorch
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods (TMI 2020)https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch
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