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Semantic segmentation is a critical method in the field of autonomousdriving. When performing semantic image segmentation, a wider field of view(FoV) helps to obtain more information about the surrounding environment,making automatic driving safer and more reliable, which could be offered byfisheye cameras. However, large public fisheye datasets are not available, andthe fisheye images captured by the fisheye camera with large FoV comes withlarge distortion, so commonly-used semantic segmentation model cannot bedirectly utilized. In this paper, a seven degrees of freedom (DoF) augmentationmethod is proposed to transform rectilinear image to fisheye image in a morecomprehensive way. In the training process, rectilinear images are transformedinto fisheye images in seven DoF, which simulates the fisheye images taken bycameras of different positions, orientations and focal lengths. The resultshows that training with the seven-DoF augmentation can improve the model'saccuracy and robustness against different distorted fisheye data. Thisseven-DoF augmentation provides a universal semantic segmentation solution forfisheye cameras in different autonomous driving applications. Also, we providespecific parameter settings of the augmentation for autonomous driving. Atlast, we tested our universal semantic segmentation model on real fisheyeimages and obtained satisfactory results. The code and configurations arereleased at https://github.com/Yaozhuwa/FisheyeSeg.

Universal Semantic Segmentation for Fisheye Urban Driving Images