self-supervised-depth-completion and Sparse-Depth-Completion
These are competitors offering alternative approaches to the same task: both perform sparse-to-dense depth completion by fusing LiDAR and monocular RGB data, with the key difference being that fangchangma's method is self-supervised while wvangansbeke's achieves superior KITTI benchmark performance through supervised learning.
Maintenance
0/25
Adoption
10/25
Maturity
16/25
Community
25/25
Maintenance
0/25
Adoption
10/25
Maturity
16/25
Community
21/25
Stars: 650
Forks: 134
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: 506
Forks: 77
Downloads: —
Commits (30d): 0
Language: Python
License: —
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About self-supervised-depth-completion
fangchangma/self-supervised-depth-completion
ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera"
About Sparse-Depth-Completion
wvangansbeke/Sparse-Depth-Completion
Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]
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