MultiDIC: an Open-Source Toolbox for Multi-View 3D Digital Image Correlation

Abstract

Three-dimensional Digital Image Correlation (3D-DIC) is a non-contact optical-numerical technique for evaluating the dynamic mechanical behavior at the surface of structures and materials, including biological tissues. 3D-DIC can be used to extract shape and full-?eld displacements and strains with high resolution, at various length scales. While various commercial and academic 3D-DIC software exist, the field lacks 3D-DIC packages which offer straightforward calibration and data-merging solutions for multi-view analysis, which is particularly desirable in biomedical applications. To address these limitations, we present MultiDIC, an open-source MATLAB toolbox, featuring the first 3D-DIC software specifically dedicated to multi-view setups. MultiDIC integrates robust two-dimensional subset-based DIC software with specially tailored calibration procedures, to reconstruct the dynamic behavior of surfaces from multiple stereo-pairs. MultiDIC contains novel algorithms to automatically merge meshes from multiple stereopairs, and to compute and visualize 3D shape and full-?eld motion, deformation, and strain. User interfaces provide capabilities to perform 3D-DIC analyses without interacting with MATLAB syntax, while standalone functions also allow proficient MATLAB users to write custom scripts for specific experimental requirements. This paper discusses the challenges underlying multi-view 3D-DIC, details the proposed solutions, and describes the algorithms implemented in MultiDIC. The performance of MultiDIC is tested using a low-cost experimental system featuring a 360-deg 12-camera setup. The software and system are evaluated using measurement of a cylindrical object with known geometry subjected to rigid body motion and measurement of the lower limb of a human subject. The findings confirm that shape, motion, and full-field deformations and strains can be accurately measured, and demonstrate the feasibility of MultiDIC in multi-view in-vivo biomedical applications.

A Framework for Measuring the Time-Varying Shape and Full-Field Deformation of Residual Limbs Using 3-D Digital Image Correlation

“…A stereo camera rig was designed and fabricated, to hold up to 40 camera units. Each unit contains a Raspberry Pi Camera Module V2, featuring a Sony IMX219 8-megapixel 3.674 2.760 mm CMOS sensor, connected to a Raspberry Pi Zerow (see also [21]). All cameras were connected and synchronously controlled using an Ethernet hub connected to a PC.…”

“…Prior to human subject testing, the camera configuration was verified by conducting preliminary tests on residual limb replicas (plaster cast molds), in order to ensure sufficient coverage and reconstruction accuracy. In the previous study, only 12 cameras, all having 30° between them, were used radially [21]. However, it was found that a narrower angle between the cameras is necessary for successfully reconstructing the anterior surface of the limb, especially around the anterior part of the knee, since these regions are typically characterized by higher curvatures than the rest of the limb.…”

“…Existing 3D-DIC software packages are typically designed for systems with a small number of cameras (2-4), and do not provide a practical solution for multi-camera systems, which are necessary for imaging the entire residuum surface. To solve this problem, a recent work by the authors developed an opensource 3D-DIC software, named MultiDIC, which allows for the reconstruction of the dynamic behavior of surfaces from multiple cameras [21]. MultiDIC was specifically tailored for experimental setups consisting of a large number of cameras.…”

“…In the present study, the experimental setup developed in our previous study [21] was modified to incorporate a larger number of cameras, adequate for measuring transtibial residual limbs, and a new custom calibration object was designed and fabricated to accommodate the modified camera setup. This paper presents the new experimental setup and the data analysis methods, which were developed to evaluate not only the deformation and strain distributions, but also the volumes and cross-sectional areas of the residuum.…”

Six‐degree‐of‐freedom generalized displacements measurement based on binocular vision

“…Therefore, vision‐based sensing technology has been widely adopted in recent years for measuring the static and dynamic displacements of structural systems. For example, digital image correlation can provide high accuracy deformation measurement with high space resolution . Chen et al conducted a dynamic displacement‐field measurement based on RGB‐D sensor.…”