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:

1. 3D Scene Shape

You may want to check this video which provides a very brief introduction to the work:

RGB	Depth	Point Cloud

2. 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.