Argos：适应丰富几何先验以实现可泛化的在线场景变化检测
Argos: Adapt Rich Geometric Priors for Generalizable Online Scene-Change-Detection
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- MIT（麻省理工学院）
- Gwangju Institute of Science and Technology（光州科学技术院）
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中文总结 AI 辅助
Argos利用几何基础模型的隐式3D知识，联合场景变化检测与重建，引入大规模基准和Argos-SLAM实时系统，显著提升跨域泛化性能。
中文摘要 AI 辅助
在动态环境中运行的机器人需要可靠地检测其周围环境随时间的变化。现有的基于学习的方法主要依赖于成对的二维图像特征，这些方法在大视角变化和遮挡下表现不佳，对噪声敏感，且跨域泛化能力有限，而显式的三维方法通常需要昂贵的离线优化。我们表明，几何基础模型（GFMs）的隐式三维知识为解决这些局限性提供了坚实的基础。我们引入了Argos，它适应GFM特征以进行联合场景变化检测和三维重建。为了解决数据稀缺问题并朝着场景变化检测的基础模型迈出一步，我们引入了一个大规模基准，包含两个合成数据集和一个真实世界数据集，并在不同数据集上联合训练以提高跨域泛化能力。我们进一步引入了Argos-SLAM，一个专为机器人设计的实时系统，它执行在线变化检测和变化感知的四维建图。在基准测试中，我们的框架显著优于现有基线，在变化IoU上提升高达42.01%，在F1分数上提升27.91%，同时支持在不断变化的真实世界环境中进行可扩展部署。
英文摘要
Robots operating in dynamic environments require reliable detection of how their surroundings change over time. Existing learning-based methods largely rely on pairwise 2D image features, which struggle under large viewpoint changes and occlusions, are sensitive to noise, and show limited generalization across domains, while explicit 3D approaches typically require costly offline optimization. We show that the implicit 3D knowledge of Geometric Foundation Models (GFMs) provides a strong basis for addressing these limitations. We introduce Argos, which adapts GFM features for joint scene change detection and 3D reconstruction. To address data scarcity and take a step toward a foundation model for scene change detection, we introduce a large-scale benchmark comprising two synthetic datasets and one real-world dataset, and train jointly across diverse datasets to improve cross-domain generalization. We further introduce Argos-SLAM, a real-time system designed for robotics, which performs online change detection and change-aware 4D mapping. Across benchmarks, our framework substantially outperforms existing baselines, with gains of up to 42.01% in change IoU and 27.91% in F1, while supporting scalable deployment in changing real-world environments.
