-
[pdf]
[supp]
[bibtex]@InProceedings{Qiao_2023_ICCV, 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}, title = {Multi-view Spectral Polarization Propagation for Video Glass Segmentation}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {23218-23228} }
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.
Related Material