surface_normal_uncertainty
ipme
| surface_normal_uncertainty | ipme | |
|---|---|---|
| 1 | 1 | |
| 208 | 24 | |
| - | - | |
| 10.0 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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surface_normal_uncertainty
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Non official colab to create normal maps using "Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation" from baegwangbin
I needed normal maps for my movie and I saw in the new ControlNet update that they used https://github.com/baegwangbin/surface_normal_uncertainty to make normals which gave me better results than previous methods. So I decided to make a colab to process all my images because I didn't find how to do it in Automatic 1111.
ipme
What are some alternatives?
2dimageto3dmodel - We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
EOmaps - A library to create interactive maps of geographical datasets
fortuna - A Library for Uncertainty Quantification.
deep-kernel-transfer - Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
IGR - Implicit Geometric Regularization for Learning Shapes
uncertainty-toolbox - Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
DIML - [ICCV 2021] Towards Interpretable Deep Metric Learning with Structural Matching
neural-deferred-shading - Multi-View Mesh Reconstruction with Neural Deferred Shading (CVPR 2022)
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)