  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 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
the flexibility to be adapted to other sensor combination scenarios.
