Repository for our CVPR'22 ScaleNet paper:

"ScaleNet: A Shallow Architecture for Scale Estimation".
Axel Barroso-Laguna, Yurun Tian, and Krystian Mikolajczyk. arxiv 2021.

[Paper on arxiv]

Python 3.7 is required for running and training ScaleNet code. Use Conda to install the dependencies:

conda create --name scalenet_env
conda activate scalenet_env 
conda install pytorch==1.2.0 -c pytorch
conda install -c conda-forge tensorboardx opencv tqdm 
conda install -c anaconda pandas 
conda install -c torchvision 

run_scalenet.py can be used to estimate the scale factor between two input images. We provide as an example two images, im1.jpg and im2.jpg, within the assets/im_test folder as an example. For a quick test, please run:

python run_scalenet.py --im1_path assets/im_test/im1.jpg --im2_path assets/im_test/im2.jpg

Arguments:

• im1_path: Path to image A.
• im2_path: Path to image B.

It returns the scale factor A->B.

We provide a list of Megadepth image pairs and scale factors in the assets folder. We use the undistorted images, corresponding camera intrinsics, and extrinsics preprocessed by D2-Net. You can download them directly from their main repository. If you desire to use the default configuration for training, just run the following line:

python train_ScaleNet.py --image_data_path /path/to/megadepth_d2net

There are though some important arguments to take into account when training ScaleNet.

Arguments:

• image_data_path: Path to the undistorted Megadepth images from D2-Net.
• save_processed_im: ScaleNet processes the images so that they are center-cropped and resized to a default resolution. We give the option to store the processed images and load them during training, which results in a much faster training. However, the size of the files can be big, and hence, we suggest storing them in a large storage disk. Default: True.
• root_precomputed_files: Path to save the processed image pairs.

If you desire to modify ScaleNet training or architecture, look for all the arguments in the train_ScaleNet.py script.

In addition to the training, we also provide a template for testing ScaleNet in the camera pose task. In assets/data/test.csv, you can find the test Megadepth pairs, along with their scale change as well as their camera poses.

Run the following command to test ScaleNet + SIFT in our custom camera pose split:

python test_camera_pose.py --image_data_path /path/to/megadepth_d2net

camera_pose.py script is intended to provide a structure of our camera pose experiment. You can change either the local feature extractor or the scale estimator and obtain your camera pose results.

If you use this code or the provided training/testing pairs in your research, please cite our paper:

@inproceedings{barroso2022scalenet,
  title={ScaleNet: A Shallow Architecture for Scale Estimation},
  author={Barroso-Laguna, Axel and Tian, Yurun and Mikolajczyk, Krystian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12808--12818},
  year={2022}
}