AdelaiDepth
AdelaiDepth is an open source toolbox for monocular depth prediction. Relevant work from our group is open-sourced here.
AdelaiDepth contains the following algorithms:
- Boosting Depth: Code, Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth (Boosting Monocular Depth Estimation with Sparse Guided Points)
- 3D Scene Shape (Best Paper Finalist): Code, Learning to Recover 3D Scene Shape from a Single Image
- DiverseDepth: Code, Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction, DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data
- Virtual Normal: Code, Enforcing geometric constraints of virtual normal for depth prediction
- Depth Estimation Using Deep Convolutional Neural Fields: Code, Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields, TPAMI'16, CVPR'15
News:
- [May. 31, 2022] Code for local recovery strategy of BoostingDepth is released.
- [May. 31, 2022] Training code and data of LeReS project have been released.
- [Feb. 13, 2022] Training code and data of DiverseDepth project have been released.
- [Jun. 13, 2021] Our "Learning to Recover 3D Scene Shape from a Single Image" work is one of the CVPR'21 Best Paper Finalists.
- [Jun. 6, 2021] We have made the training data of DiverseDepth available.
Results and Dataset Examples:
- 3D Scene Shape
You may want to check this video which provides a very brief introduction to the work:
| RGB | Depth | Point Cloud |
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 |
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- DiverseDepth
- Results examples：
- DiverseDepth dataset examples：
BibTeX
@article{yin2022towards,
title={Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image},
author={Yin, Wei and Zhang, Jianming and Wang, Oliver and Niklaus, Simon and Chen, Simon and Liu, Yifan and Shen, Chunhua},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year={2022}
}
@inproceedings{Yin2019enforcing,
title = {Enforcing geometric constraints of virtual normal for depth prediction},
author = {Yin, Wei and Liu, Yifan and Shen, Chunhua and Yan, Youliang},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
year = {2019}
}
@inproceedings{Wei2021CVPR,
title = {Learning to Recover 3D Scene Shape from a Single Image},
author = {Wei Yin and Jianming Zhang and Oliver Wang and Simon Niklaus and Long Mai and Simon Chen and Chunhua Shen},
booktitle = {Proc. IEEE Conf. Comp. Vis. Patt. Recogn. (CVPR)},
year = {2021}
}
@article{yin2021virtual,
title = {Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction},
author = {Yin, Wei and Liu, Yifan and Shen, Chunhua},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year = {2021}
}Contact
- Wei Yin https://yvanyin.net/
- Chunhua Shen https://cshen.github.io
License
The 3D Scene Shape code is under a non-commercial license from Adobe Research. See the LICENSE file for details.
Other depth prediction projects are licensed under the 2-clause BSD License for non-commercial use -- see the LICENSE file for details. For commercial use, please contact Chunhua Shen.