Skip to content

Latest commit

 

History

History
103 lines (73 loc) · 4.14 KB

README.md

File metadata and controls

103 lines (73 loc) · 4.14 KB
Raw

IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty

Official implementation of the paper

IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty
BMVC 2022
Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla
[arXiv] [demo] [project page]

Summary

  • We use surface normal to propagate depth between pixels.
  • We formulate depth refinement/upsampling as classification of choosing the neighboring pixel to propagate from.

Getting Started

We recommend using a virtual environment.

python3.6 -m venv --system-site-packages ./venv
source ./venv/bin/activate

Install the necessary dependencies by

python3.6 -m pip install -r requirements.txt

Go to this google drive, and

  • Download *.pt and place them under ./checkpoints.
  • Download and unzip examples.zip as ./examples.

Testing

# test on scannet images, using the model trained on scannet
python test.py --train_data scannet --test_data scannet

# test on nyuv2 images, using the model trained on nyuv2
python test.py --train_data nyuv2 --test_data nyuv2

# test on your own images, using the model trained on scannet
python test.py --train_data scannet --test_data custom
  • This generates output visualizations under ./examples/output/dataset_name/.
  • Comment out unnecessary visualization scripts to speed things up.
  • When testing on your own images, you should place the images under ./examples/data/custom/. We support .png and .jpg files. If you wish to provide the camera intrinsics, add a file named img_name.txt. The file should contain fx, fy, cx and cy. See ./examples/data/custom/ex01.txt as an example.

Training

We provide the training script for ScanNet images. It is straightforward to apply the same code for other datasets.

Step 1. Data Preparation

Firstly, go to this google drive. Download and unzip scannet.zip as ./scannet. The folder has two sub-folders named train and test. For each of them, there is a set of scenes. Images in each scene are assumed to be taken with the same camera. The camera intrinsics (fx, fy, cx, cy) should be provided as intrins.txt. For each image, you should have four files:

  • 000000_img.png: RGB image
  • 000000_depth.png: GT depth map
  • 000000_norm.png: Predicted normal map
  • 000000_kappa.png: Predicted normal uncertainty

We generated normal predictions offline instead of generating them on the fly. If you have a dataset with no surface normal prediction, add additional scenes/images and run

python preprocess.py

Step 2. Training

To train the network, run

python train.py

Note that the provided scannet mini dataset only contains 100 images for training and 10 images for testing. You should train the network on a bigger dataset to obtain satisfactory results.

Citation

If you find our work useful in your research please consider citing our papers:

@InProceedings{Bae2022,
    title   = {IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty}
    author  = {Gwangbin Bae and Ignas Budvytis and Roberto Cipolla},
    booktitle = {British Machine Vision Conference (BMVC)},
    year = {2022}                         
}

@InProceedings{Bae2021,
    title   = {Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation}
    author  = {Gwangbin Bae and Ignas Budvytis and Roberto Cipolla},
    booktitle = {International Conference on Computer Vision (ICCV)},
    year = {2021}                         
}