alr-internship/self-supervised-depth-denoising
Denoising YCB Objects with a self-supervised deep neural network
This project helps robotics engineers and researchers improve the clarity of 3D depth sensor data. It takes in noisy, low-quality (LQ) depth images of objects, like those from robotic vision systems, and outputs significantly cleaner, higher-quality (HQ) depth images. This is particularly useful for tasks involving precise object manipulation or scene understanding in robotics.
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Use this if you need to transform noisy 3D depth data from robotic sensors into clean, precise depth maps for better object recognition or interaction.
Not ideal if your application doesn't involve 3D depth sensor data or if you need to denoise standard 2D images.
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Jupyter Notebook
License
MIT
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Last pushed
Mar 01, 2025
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