System to measure three-dimensional movements in physical models

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. used to capture images of a deforming soil surface. The images were used to reconstruct the observed scene to a high density, accurate 3D point cloud. A new method has been developed to process the obtained 3D point clouds and images to determine 3D displacement vectors. The procedure is highly automatic which allows large data sets to be processed with minimal manual intervention. Two series of quantification experiments were carried out to assess the performance of the system which showed the overall accuracy to be within 0.05mm over a field of view of Permanent repository link500×250mm. An example application is presented to demonstrate the capabilities of the 3D imaging system.

A study on performance of three-dimensional imaging system for physical models

“…The effect of camera resolution seems to be not significant to the measurement precision as IPhone 6s (12Mega-pixel) and DSLR Canon (18Mega-pixel) yielded similar precisions which are in line with the results reported by Galland et al (2016) and Le et al (2016). It can be seen that camera resolution is not the only factor that governs the quality of images and hence the quality of 3D point clouds.…”

“…Galland et al (2016) used Micmac to analyse four images taken by 24 Mega-Pixel cameras and the achieved measurement precision was 50µm. Similarly, Le et al (2016) reported a precision of 50µm was achieved by using Micmac to analyse three images, taken by three 2Mega-pixel cameras.…”

“…The test images were undistorted and unwarped before being analysed by conventional 2D PIV (Stanier et al 2016). The detailed procedure is described by Le et al (2016) and illustrated in Figure 4.…”

“…For large scale, Smith et al (2015) reported SfM-MVS were used in 3D topographic surveys, monitoring glacier movements, observing and tracking lava movements and landslide displacements. For small scale such as experiments in laboratory, Galland et al (2016) and Le et al (2016) used SfM-MVS to measure three-dimensional displacements.…”

“…Simply put, SfM-MVS processes a minimum of two images to reproduce the 3D point cloud of the observed scene. The SfM-MVS algorithm is described in detail by Robertson & Cipolla (2009) and Le et al (2016). The fundamental principles are illustrated in Figure 1 and described briefly below.…”