Argos: Adapt Rich Geometric Priors for Generalizable Online Scene-Change-Detection

Key points

• Robots operating in dynamic environments require reliable detection of how their surroundings change over time.1
• We show that the implicit 3D knowledge of Geometric Foundation Models (GFMs) provides a strong basis for addressing these limitations.1
• 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.1
• We further introduce Argos-SLAM, a real-time system designed for robotics, which performs online change detection and change-aware 4D mapping.1

Sources (1)

• [1]Argos: Adapt Rich Geometric Priors for Generalizable Online Scene-Change-Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:48 PM

  We introduce Argos, which adapts GFM features for joint scene change detection and 3D reconstruction.

  Robots operating in dynamic environments require reliable detection of how their surroundings change over time.

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