fangchangma/self-supervised-depth-completion
ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera"
This helps autonomous vehicles and robotics perceive their surroundings in fine detail. It takes sparse depth data from a LiDAR sensor and a standard camera image, then fills in the gaps to create a complete, dense depth map. Roboticists and autonomous vehicle engineers can use this to give their systems a more comprehensive understanding of 3D space.
650 stars. No commits in the last 6 months.
Use this if you need to generate detailed, full-scene depth maps for your robots or self-driving cars using readily available sensor data.
Not ideal if you're not working with LiDAR and monocular camera inputs, or if you need depth information for non-automotive/robotics applications.
Stars
650
Forks
134
Language
Python
License
MIT
Category
Last pushed
Apr 24, 2021
Commits (30d)
0
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