AutoAILab/FusionDepth
Official implementation for paper "Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"
This project helps autonomous robotics engineers and researchers accurately perceive 3D environments. It takes monocular camera images and sparse LiDAR data (e.g., from a 4-beam sensor) as input to generate highly detailed 3D depth maps. This allows robots to understand the exact distance to every object in their field of view, which is crucial for navigation, obstacle avoidance, and object detection.
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Use this if you need to generate highly accurate, dense 3D depth maps from standard camera footage combined with low-cost sparse LiDAR for autonomous systems.
Not ideal if you do not have access to sparse LiDAR data or if your application requires real-time processing on extremely constrained hardware.
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Language
Python
License
MIT
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Last pushed
Jul 29, 2022
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