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Direct image-to-image alignment that relies on the optimization of photometric error metrics suffers from limited convergence range and sensitivity to lighting conditions.
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A benchmark for the evaluation of rgb-d slam systems
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Semi-dense visual odometry for a monocular camera
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Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation
C. Bailer, B. Taetz, and D. Stricker · 2015
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A. Kendall, M. Grimes, and R. Cipolla · 2015
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O. Ronneberger, P. Fischer, and T. Brox · 2015
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Photometric bundle adjustment for vision-based slam
H. Alismail, B. Browning, and S. Lucey · 2016
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Relative camera pose estimation using convolutional neural networks
I. Melekhov, J. Ylioinas, J. Kannala, and E. Rahtu · 2017
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Automatic differentiation in pytorch
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Cnn-slam: Real-time dense monocular slam with learned depth prediction
K. Tateno, F. Tombari, I. Laina, and N. Navab · 2017
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Demon: Depth and motion network for learning monocular stereo
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F. Yu, V. Koltun, and T. A. Funkhouser · 2017
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Codeslam-learning a compact, optimisable representation for dense visual slam
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Clkn: Cascaded lucas-kanade networks for image alignment
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