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

2026-09-19

AI Summary

提出了一种无需训练的方法，利用基础模型的置信度预测和单目深度估计来解决未知物体的检测与分割问题。

Abstract

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 predictions of a foundation segmentation model, without any task-specific fine-tuning or access to anomalous data. To improve the robustness of our OOD segmentation, geometric information from monocular depth estimation is incorporated into the decision process, providing complementary cues to uncertainty-based predictions. We evaluate the proposed method on the SegmentMeIfYouCan benchmark and additionally assess its performance on OOD tracking in video sequences, reflecting the temporal nature of real-world perception systems. The method performs strongly on road-centered benchmarks.

Problem

• out-of-distribution
• autonomous vehicles
• open-world scenarios
• unknown objects
• segmentation

Innovation

• Training-Free
• Foundation Model Confidence
• Monocular Depth Estimation
• Out-of-Distribution Segmentation