  Autonomous vehicles require a precise understanding of their environment to
navigate safely. Reliable identification of unknown objects, especially those
that are absent during training, such as wild animals, is critical due to their
potential to cause serious accidents. Significant progress in semantic
segmentation of anomalies has been driven by the availability of
out-of-distribution (OOD) benchmarks. However, a comprehensive understanding of
scene dynamics requires the segmentation of individual objects, and thus the
segmentation of instances is essential. Development in this area has been
lagging, largely due to the lack of dedicated benchmarks. To address this gap,
we have extended the most commonly used anomaly segmentation benchmarks to
include the instance segmentation task. Our evaluation of anomaly instance
segmentation methods shows that this challenge remains an unsolved problem. The
benchmark website and the competition page can be found at:
https://vision.rwth-aachen.de/oodis .
