ICCV2019_MirrorNet

Where Is My Mirror? (ICCV2019)

Xin Yang*, Haiyang Mei*, Ke Xu, Xiaopeng Wei, Baocai Yin, Rynson W.H. Lau^† (* Joint first authors, ^† Rynson Lau is the corresponding author and he led this project.)

[Project Page][Arxiv]

Abstract

Mirrors are everywhere in our daily lives. Existing computer vision systems do not consider mirrors, and hence may get confused by the reflected content inside a mirror, resulting in a severe performance degradation. However, separating the real content outside a mirror from the reflected content inside it is non-trivial. The key challenge lies in that mirrors typically reflect contents similar to their surroundings, making it very difficult to differentiate the two. In this paper, we present a novel method to accurately segment mirrors from an input image. To the best of our knowledge, this is the first work to address the mirror segmentation problem with a computational approach. We make the following contributions. First, we construct a large-scale mirror dataset that contains mirror images with the corresponding manually annotated masks. This dataset covers a variety of daily life scenes, and will be made publicly available for future research. Second, we propose a novel network, called MirrorNet, for mirror segmentation, by modeling both semantical and low-level color/texture discontinuities between the contents inside and outside of the mirrors. Third, we conduct extensive experiments to evaluate the proposed method, and show that it outperforms the carefully chosen baselines from the state-of-the-art detection and segmentation methods.

Citation

If you use this code or our dataset (including test set), please cite:

@InProceedings{Yang_2019_ICCV,
    author = {Yang, Xin and Mei, Haiyang and Xu, Ke and Wei, Xiaopeng and Yin, Baocai and Lau, Rynson W.H.},
    title = {Where Is My Mirror?},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2019}
}

Dataset

See Peoject Page

Requirements

• PyTorch == 0.4.1
• TorchVision == 0.2.1
• CUDA 9.0 cudnn 7
• Setup

sudo pip3 install -r requirements.txt
git clone https://github.com/Mhaiyang/dss_crf.git
sudo python setup.py install

Test

Download trained model MirrorNet.pth at here, then run infer.py.

Updated Main Results

Quantitative Results

Component analysis

Qualitative Results

License

Please see license.txt

Contact

E-Mail: [email protected]