Multi-View Spectral Polarization Propagation for Video Glass Segmentation
Abstract
In this paper, we present the first polarization-guided video glass segmentation propagation solution (PGVS-Net) that can robustly and coherently propagate glass segmentation in RGB-P video sequences. By leveraging spatiotemporal polarization and color information, our method combines multi-view polarization cues and thus can alleviate the view dependence of single-input intensity variations on glass objects. We demonstrate that our model can outperform glass segmentation on RGB-only video sequences as well as produce more robust segmentation than per-frame RGB-P single-image segmentation methods. To train and validate PGVS-Net, we introduce a novel RGB-P Glass Video dataset (PGV-117) containing 117 video sequences of scenes captured with different types of camera paths, lighting conditions, dynamics, and glass types.
Cite
Text
Qiao et al. "Multi-View Spectral Polarization Propagation for Video Glass Segmentation." International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.02122Markdown
[Qiao et al. "Multi-View Spectral Polarization Propagation for Video Glass Segmentation." International Conference on Computer Vision, 2023.](https://mlanthology.org/iccv/2023/qiao2023iccv-multiview/) doi:10.1109/ICCV51070.2023.02122BibTeX
@inproceedings{qiao2023iccv-multiview,
title = {{Multi-View Spectral Polarization Propagation for Video Glass Segmentation}},
author = {Qiao, Yu and Dong, Bo and Jin, Ao and Fu, Yu and Baek, Seung-Hwan and Heide, Felix and Peers, Pieter and Wei, Xiaopeng and Yang, Xin},
booktitle = {International Conference on Computer Vision},
year = {2023},
pages = {23218-23228},
doi = {10.1109/ICCV51070.2023.02122},
url = {https://mlanthology.org/iccv/2023/qiao2023iccv-multiview/}
}