# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 1 Introduction
On the other hand, the stereo camera pairs are independent from each other, thus, it is only suitable for dual-surface displacement field measurement of sheet specimens. (iv) Mirror-assisted configuration. A 360-deg panoramic or dual-surface view of the regular-sized specimen to be measured is obtained through specific catadioptric components, which is in the form of a planar mirror [12; 19; 20] or conical mirror [5; 9; 21]. The mirror-assisted configuration can be used for panoramic measurement, but only for specimens of comparable mirror size, and there are still challenges on shape and displacement measurement of larger objects. In addition, the entailed wide field of view required conflicts with the high spatial resolution of region of interest (ROI) required for accurate DIC measurement. Despite the different configurations and applications for 360-deg panoramic shape and deformation measurements that can be found, the three key issues involved in achieving a multi-view panoramic setup tend to be similar and consist mainly of stereo camera calibration, spatial and temporal matching, and 3D reconstruction. The aim of stereo camera calibration is to find intrinsic parameters (defining the geometric and optical properties of the camera) and extrinsic parameters (defining the position and orientation of the camera with respect to a reference coordinate system). According to different scenes, the existing calibration algorithms can be divided into four methods: self-calibration [22; 23; 24; 25], calibration based on 1D calibration objects [26; 27; 28], 2D calibration objects [29; 30; 31], and 3D calibration objects [32; 33; 34], respectively. It should be noted that the translation vector calibrated by the above calibration method is normalized and lacks scale information, which is necessary to determine the actual value of the shape and/or displacement. In this paper, with the aid of the laser point clouds, the scale factor calculation model is established by calculating the center of gravity, the centroid of the laser point cloud, and the reconstructed 3D point cloud and its corresponding relations. Spatial matching focuses on establishing a three-dimensional correspondence on two different camera views, while the aim of temporal matching is to track the pixel positionson each image sequence, both of which can be achieved by robust local feature-based image correlation algorithms [35; 36]. In this paper, precise subset-based DIC following the inverse compositional Gauss-Newton algorithm and the reliability-guided displacement computation strategy is used for spatial and temporal matching; the results of spatial matching are used to establish the correspondences for extrinsic parameter estimation and to reconstruct the 3D surface profile of the measured specimen, while the results of temporal matching are used to calculate the displacement before and after deformation. A key step of panoramic shape and displacement measurement is to merge multiple local 3D surfaces (reconstructed 3D coordinates) with overlapping regions obtained based on multi-view Stereo-DIC into a single continuous merged surface. The essence of surface merging is to merge overlapping parts by removing redundant points as to obtain the complete 3D profile information of objects. The principle of surface merging is to use only the points existing in the original data. The introduction of new points not derived from actual measurements is undesirable as these points cannot be tracked in a deformed configuration and do not provide reliable displacement measurements [15]. For this reason, we propose a distance-based method for merging 3D surfaces (3D points) assisted by a laser point cloud. Based on the existing methods, this method proposes a laser point cloud data-assisted scale factor determination method and a laser point cloud data-assisted surface stitching method to achieve a true and complete shape reconstruction and full-field displacement measurement of the object under the test.