/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.

Primary LanguagePythonCreative Commons Zero v1.0 UniversalCC0-1.0

AdelaiDepth

Open In Colab

AdelaiDepth is an open source toolbox for monocular depth prediction. Relevant work from our group is open-sourced here.

AdelaiDepth contains the following algorithms:

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

Depth

  1. DiverseDepth
  • Results examples:

Depth

  • DiverseDepth dataset examples:

DiverseDepth dataset

BibTeX

@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

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.