OoDIS: Anomaly Instance Segmentation and Detection Benchmark

TL;DR

This paper introduces OoDIS, a new benchmark for anomaly instance segmentation and detection, addressing a critical gap in evaluating how well models identify and segment unknown objects in complex scenes for autonomous navigation.

Contribution

It extends existing anomaly segmentation benchmarks to include instance segmentation and object detection, providing a comprehensive evaluation platform for these tasks.

Findings

Both anomaly instance segmentation and detection remain unsolved challenges.

Current methods show significant room for improvement in identifying unknown objects.

The benchmark facilitates future research in safe autonomous navigation.

Abstract

Safe navigation of self-driving cars and robots requires a precise understanding of their environment. Training data for perception systems cannot cover the wide variety of objects that may appear during deployment. Thus, reliable identification of unknown objects, such as wild animals and untypical obstacles, is critical due to their potential to cause serious accidents. Significant progress in semantic segmentation of anomalies has been facilitated 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. The situation is similar in object detection. While there is interest in detecting and potentially tracking every anomalous object, the availability of dedicated benchmarks is clearly limited. To address this gap, this work extends some commonly used anomaly segmentation benchmarks to include the instance segmentation and object detection tasks. Our evaluation of anomaly instance segmentation and object detection methods shows that both of these challenges remain unsolved problems. We provide a competition and benchmark website under https://vision.rwth-aachen.de/oodis