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VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization, Geometric loss functions for camera pose regression with deep learning, PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
Download the Cambridge Landmarks King's College dataset from here.
Download the starting and trained weights from here.
To run:
Extract the King's College dataset to wherever you prefer
Extract the starting and trained weights to wherever you prefer
If you want to retrain, simply run train.py
If you just want to test, simply run test.py
References
Ronald Clark, Sen Wang, Andrew Markham, Niki Trigoni, Hongkai Wen. VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization. CVPR 2017.
Alex Kendall and Roberto Cipolla. Geometric loss functions for camera pose regression with deep learning. CVPR, 2017.
Alex Kendall, Matthew Grimes and Roberto Cipolla. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization. ICCV, 2015.
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization, Geometric loss functions for camera pose regression with deep learning, PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization