[AAAI 2023] Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint. [Paper].
[Youtube] and [Bilibili]
pip install -r requirements.txt
- This repo includes GOCor as git submodule. You need to pull submodules with
git submodule update --init --recursive
git submodule update --recursive --remote
Please refer to Content-Aware Unsupervised Deep Homography Estimation..
- Dataset download links: [GoogleDriver], [BaiduYun] (key:gvor)
- Unzip the data to directory "./dataset"
- Run "video2img.py"
Be sure to scale the image to (640, 360) since the point coordinate system is based on the (640, 360).
e.g. img = cv2.imresize(img, (640, 360))
- Using the images in "train.txt" and "test.txt" for training and evaluation, the manually labeled evaluation files can be download from: [GoogleDriver], [BaiduYun](key:i721).
The models provided below are the retrained version(with minor differences in quantitative results)
| model | RE | LT | LL | LF | SF | Avg | Model |
|---|---|---|---|---|---|---|---|
| Pre-trained | 1.66 | 5.49 | 4.11 | 7.57 | 6.95 | 5.16 | Baidu [Google] |
You need to modify dataset/data_loader.py slightly for your environment, and then
python train.py --model_dir experiments/base_model/
python evaluate.py --model_dir experiments/base_model/ --restore_file xxx.pth
If you use this code or ideas from the paper for your research, please cite our paper:
@InProceedings{jiang_2023_aaai,
author = {Jiang, Hai and Li, Haipeng and Lu, Yuhang and Han, Songchen and Liu, Shuaicheng},
title = {Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint}},
booktitle = {Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI)}
year = {2023}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent