This is the official implementation of our ICCV2021 paper GyroFlow. We also provide a PyTorch version, check at GyroFlow-PyTorch
Existing optical flow methods are erroneous in challenging scenes, such as fog, rain, and night because the basic optical flow assumptions such as brightness and gradient constancy are broken. To address this problem, we present an unsupervised learning approach that fuses gyroscope into optical flow learning. Specifically, we first convert gyroscope readings into motion fields named gyro field. Second, we design a self-guided fusion module to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details. To the best of our knowledge, this is the first deep learning-based framework that fuses gyroscope data and image content for optical flow learning. To validate our method, we propose a new dataset that covers regular and challenging scenes. Experiments show that our method outperforms the state-of-art methods in both regular and challenging scenes.
- MegEngine==1.6.0
- Other requirements please refer to
requirements.txt.
2021.11.15: We release the GOF_Train V1 that contains 2000 samples.
2022.06.22: We release the PyTorch Version, welcome to have a try.
2023.07.28: Please check to GyroFlow-PyTorch for Data Preparation
To train the model, you can just run:
python train.py --model_dir experiments
Load the pretrained checkpoint and run:
python evaluate.py --model_dir experiments --restore_file experiments/val_model_best.pkl
We've updated the GOF (both trainset and testset), so the performance is a little bit different from the results reported in our paper.
MegEngine checkpoint can be download via [Google Drive].
If you think this work is useful for your research, please kindly cite:
@InProceedings{Li_2021_ICCV,
author = {Li, Haipeng and Luo, Kunming and Liu, Shuaicheng},
title = {GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {12869-12878}
}
In this project we use (parts of) the official implementations of the following works:
We thank the respective authors for open sourcing their methods.
