VFM-SDM: A vision foundation model-based framework for training-free, marker-free, and calibration-free structural displacement measurement

TL;DR

This paper introduces VFM-SDM, a novel vision foundation model-based framework for non-contact, training-free, and calibration-free structural displacement measurement, enhancing real-world structural health monitoring.

Contribution

It presents a new framework that integrates VFM-inferred camera parameters and point tracking, eliminating the need for task-specific training or on-site calibration.

Findings

Achieves low amplitude errors (NRMSE: 0.11/0.12) in displacement estimation.

Demonstrates strong temporal correlation (0.86/0.88) with actual displacements.

Maintains small peak-to-peak amplitude errors (RPPAE: 0.01/0.02).

Abstract

Reliable displacement measurement is fundamental for structural health monitoring and digital engineering workflows, as it provides direct structural response information. Vision-based measurement has emerged as a promising approach for low-cost, non-contact displacement monitoring. However, its deployment often remains constrained by task-specific model training or on-site preparation, such as marker installation or manual camera calibration. This study presents a Vision Foundation Model-based framework for Structural Displacement Measurement (VFM-SDM) that integrates VFM-inferred camera parameter estimation and point tracking to reconstruct multi-directional structural displacements via triangulation without task-specific training or on-site preparation, enabling efficient non-contact deployment in real-world applications. Structural geometry constraints are incorporated to suppress physically implausible deviations and improve estimation consistency. A multi-modal field dataset collected from an in-service pedestrian bridge is introduced alongside a unified benchmarking protocol to support reproducible evaluation. Representative results show low amplitude errors (NRMSE$_{\text{range}}$: 0.11/0.12), strong temporal agreement (correlation coefficient: 0.86/0.88), and small peak-to-peak amplitude errors (RPPAE: 0.01/0.02) for vertical and lateral displacements, indicating robust performance under real-world conditions. The proposed framework advances automated, scalable displacement monitoring and lays the groundwork for VFM-enabled structural response measurements in digital twin and data-centric construction workflows.