aim-uofa / AdelaiDepth

This repo contains the projects: 'Virtual Normal', 'DiverseDepth', and '3D Scene Shape'. They aim to solve the monocular depth estimation, 3D scene reconstruction from single image problems.

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:

• 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] 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}
}

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.