fabiotosi92/Diffusion4RobustDepth
[ECCV 2024] Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions
This project helps self-driving car engineers and robotics developers enhance the accuracy of monocular depth estimation in challenging real-world scenarios. It takes existing images with clear depth information and transforms them into diverse, difficult conditions like rain, snow, low light, or reflective surfaces. The output is a robust depth estimation model capable of accurately perceiving depth even in adverse conditions.
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Use this if you need to train or fine-tune monocular depth estimation models that perform reliably in adverse weather, low-light, or complex environments with reflective objects.
Not ideal if your application primarily involves ideal, well-lit, and clear visual conditions, as the focus here is on overcoming challenging data.
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MIT
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Last pushed
Sep 28, 2024
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