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

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

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 and decision-making. Most existing approaches require access to OOD training samples, retraining of the segmentation backbone, or dedicated auxiliary architectures, limiting their practical applicability. We propose a training-free method that derives dense OOD scores directly from the confidence pr

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