Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentation

Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo. The reliable detection and segmentation of these out-of-distribution (OOD) objects is therefore crucial for a safe understanding of the environment