GitHub / sjsu-smart-lab / Self-supervised-Monocular-Trained-Depth-Estimation-using-Self-attention-and-Discrete-Disparity-Volum
Reproduction of the CVPR 2020 paper - Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume
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PURL: pkg:github/sjsu-smart-lab/Self-supervised-Monocular-Trained-Depth-Estimation-using-Self-attention-and-Discrete-Disparity-Volum
Stars: 57
Forks: 7
Open issues: 7
License: None
Language: Python
Size: 2.61 MB
Dependencies parsed at: Pending
Created at: over 4 years ago
Updated at: about 2 years ago
Pushed at: over 4 years ago
Last synced at: about 2 years ago
Topics: cityscapes-depth-estimation, discrete-disparity-volume, inplace-activated-batchnorm, kitti-dataset, monocular-depth-estimation, ordinal-regression, pytorch, self-attention, self-supervised-learning, unsupervised-learning