  We present the WoodScape fisheye semantic segmentation challenge for
autonomous driving which was held as part of the CVPR 2021 Workshop on
Omnidirectional Computer Vision (OmniCV). This challenge is one of the first
opportunities for the research community to evaluate the semantic segmentation
techniques targeted for fisheye camera perception. Due to strong radial
distortion standard models don't generalize well to fisheye images and hence
the deformations in the visual appearance of objects and entities needs to be
encoded implicitly or as explicit knowledge. This challenge served as a medium
to investigate the challenges and new methodologies to handle the complexities
with perception on fisheye images. The challenge was hosted on CodaLab and used
the recently released WoodScape dataset comprising of 10k samples. In this
paper, we provide a summary of the competition which attracted the
participation of 71 global teams and a total of 395 submissions. The top teams
recorded significantly improved mean IoU and accuracy scores over the baseline
PSPNet with ResNet-50 backbone. We summarize the methods of winning algorithms
and analyze the failure cases. We conclude by providing future directions for
the research.
