  Motion segmentation is a complex yet indispensable task in autonomous
driving. The challenges introduced by the ego-motion of the cameras, radial
distortion in fisheye lenses, and the need for temporal consistency make the
task more complicated, rendering traditional and standard Convolutional Neural
Network (CNN) approaches less effective. The consequent laborious data
labeling, representation of diverse and uncommon scenarios, and extensive data
capture requirements underscore the imperative of synthetic data for improving
machine learning model performance. To this end, we employ the PD-WoodScape
synthetic dataset developed by Parallel Domain, alongside the WoodScape fisheye
dataset. Thus, we present the WoodScape fisheye motion segmentation challenge
for autonomous driving, held as part of the CVPR 2023 Workshop on
Omnidirectional Computer Vision (OmniCV). As one of the first competitions
focused on fisheye motion segmentation, we aim to explore and evaluate the
potential and impact of utilizing synthetic data in this domain. In this paper,
we provide a detailed analysis on the competition which attracted the
participation of 112 global teams and a total of 234 submissions. This study
delineates the complexities inherent in the task of motion segmentation,
emphasizes the significance of fisheye datasets, articulate the necessity for
synthetic datasets and the resultant domain gap they engender, outlining the
foundational blueprint for devising successful solutions. Subsequently, we
delve into the details of the baseline experiments and winning methods
evaluating their qualitative and quantitative results, providing with useful
insights.
