jhornauer/GrUMoDepth
Gradient-based Uncertainty for Monocular Depth Estimation (ECCV 2022)
This project helps computer vision researchers and robotics engineers evaluate the certainty of depth predictions from a single camera image. It takes a pre-trained monocular depth estimation model and image data (like from the KITTI or NYU Depth V2 datasets) to produce an uncertainty map alongside the depth prediction. This helps users understand how reliable each depth value is in the output.
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Use this if you are a researcher or engineer working with monocular depth estimation and need to quantify the confidence or uncertainty of your model's predictions in real-world scenarios.
Not ideal if you are looking for an out-of-the-box application for general depth estimation without needing to analyze prediction uncertainty, or if you don't have existing depth estimation models.
Stars
54
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5
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 07, 2023
Commits (30d)
0
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