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We present the WoodScape fisheye semantic segmentation challenge forautonomous driving which was held as part of the CVPR 2021 Workshop onOmnidirectional Computer Vision (OmniCV). This challenge is one of the firstopportunities for the research community to evaluate the semantic segmentationtechniques targeted for fisheye camera perception. Due to strong radialdistortion standard models don't generalize well to fisheye images and hencethe deformations in the visual appearance of objects and entities needs to beencoded implicitly or as explicit knowledge. This challenge served as a mediumto investigate the challenges and new methodologies to handle the complexitieswith perception on fisheye images. The challenge was hosted on CodaLab and usedthe recently released WoodScape dataset comprising of 10k samples. In thispaper, we provide a summary of the competition which attracted theparticipation of 71 global teams and a total of 395 submissions. The top teamsrecorded significantly improved mean IoU and accuracy scores over the baselinePSPNet with ResNet-50 backbone. We summarize the methods of winning algorithmsand analyze the failure cases. We conclude by providing future directions forthe research.

Woodscape Fisheye Semantic Segmentation for Autonomous Driving -- CVPR 2021 OmniCV Workshop Challenge