Argos: 幾何学的基盤モデルの事前知識を活用した汎用オンラインシーン変化検出
Argos: Adapt Rich Geometric Priors for Generalizable Online Scene-Change-Detection
幾何学的基盤モデルの特徴を適応させ、シーン変化検出と3D再構成を同時に行う手法を提案し、大規模ベンチマークとリアルタイムSLAMシステムを構築して既存手法を大幅に上回る性能を達成した。
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著者: Ruihan Xu, Jiae Yoon, Kaichen Zhou, Ue-Hwan Kim, Luca Carlone
分類: cs.CV
原文アブストラクト
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.