OoDIS is a benchmark dataset for anomaly instance segmentation, crucial for autonomous vehicle safety. It extends existing anomaly segmentation benchmarks to focus on the segmentation of individual out-of-distribution (OOD) objects.

The dataset addresses the need for identifying and segmenting unknown objects, which are critical to avoid accidents. It includes diverse scenes with various anomalies, pushing the boundaries of current segmentation capabilities.

The benchmark is focused on evaluation of detection and instance segmentation of unexpected obstacles on roads.

For more details, refer to the OoDIS paper