Polarization-driven semantic segmentation via efficient attention-bridged fusion
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Semantic segmentation (SS) is promising for outdoor scene perception in safety-critical applications like autonomous vehicles, assisted navigation and so on. However, traditional SS is primarily based on RGB images, which limits the reliability of SS in complex outdoor scenes, where RGB images lack necessary information dimensions to fully perceive unconstrained environments. As a preliminary investigation, we examine SS in an unexpected obstacle detection scenario, which demonstrates the necessity of multimodal fusion. Thereby, in this work, we present EAFNet, an Efficient Attention-bridged Fusion Network, to exploit complementary information coming from different optical sensors. Specifically, we incorporate polarization sensing to obtain supplementary information, considering its optical characteristics for robust representation of diverse materials. By using a single-shot polarization sensor, we build the first RGB-P dataset which consists of 394 annotated pixel-aligned RGB-polarization images. A comprehensive variety of experiments shows the effectiveness of EAFNet to fuse polarization and RGB information, as well as its flexibility to be adapted to other sensor combination scenarios.
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ZJU is the only dataset among those listed supporting the light polarization modality.
“ZJU [79] is a real-world dataset and the only among the one listed supporting the light polarization modality.”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [79]
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- Authors
- Xiang, K., Yang, K., & Wang, K.
- Affiliations
- Zhejiang University (CN)
- Karlsruhe Institute of Technology (DE)
- Venue
- Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2021-02-01
- Volume
- 29
- Issue
- 4
- Pages
- 4802
- ISSN
- 1094-4087 (ISSN-L)
- OpenAlex
- W3110089052
- Cited by
- 111
- References
- 50
- Citations by year
- 2026: 20 · 2025: 20 · 2024: 26 · 2023: 18 · 2022: 20 · 2021: 7
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Optics Express (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- ZJU-Sunny Photonics Innovation Center (2020-03)
- Bundesministerium für Arbeit und Soziales (01KM151112)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- semantic segmentation, polarization sensing, multimodal fusion, obstacle detection, autonomous vehicles, attention mechanism
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