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Full-Field Surface 3D Shape and Displacement Measurements Using an Unfocused Plenoptic Camera

机译：使用未聚焦的通风摄像头全场表面3D形状和位移测量

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Full-field surface 3D shape and displacement measurements using a single commercial unfocused plenoptic camera (Lytro Illum) are reported in this work. Before measurements, the unfocused plenoptic camera is calibrated with two consecutive steps, including lateral calibration and depth calibration. Each raw image of a checkerboard pattern recorded by Lytro Illum is first extracted to an array of sub-aperture images (SAIs), and the center sub-aperture images (CSAIs) at diverse poses are used for lateral calibration to determine intrinsic and extrinsic parameters. The parallax maps between the CSAI and the remaining SAIs at each pose are then determined for depth parameters estimation using depth calibration. Furthermore, a newly developed physical-based depth distortion model is established to correct the serious distortion of the depth field. To realize shape and deformation measurements, the raw images of a test sample with speckle patterns premade on its surface are captured by Lytro Illum and extracted to arrays of SAIs. The parallax maps between the CSAI and the target SAIs are obtained using subset-based digital image correlation. Based on the pre-computed intrinsic and depth parameters and the disparity map, the full-field surface 3D shape and displacement of a test object are finally determined. The effectiveness and accuracy of the proposed approach are evaluated by a set of experiments involving the shape reconstruction of a cylinder, in-plane and out-of-plane displacement measurements of a flat plate and 3D full-field displacement measurements of a cantilever beam. The preliminary results indicate that the proposed method is expected to become a novel approach for full-field surface 3D shape and displacement measurements.
机译：在这项工作中报告了使用单一商业未聚焦的通风摄像头（Lytro Illum）的全场表面3D形状和位移测量。在测量之前，使用两个连续步骤进行校准，包括横向校准和深度校准。首先将由Lytro Illum记录的棋盘图案的每个原始图像提取到子孔径图像（SAI）的阵列，并且各种姿势的中心子孔径图像（CSAIS）用于横向校准以确定内在和外部参数。然后使用深度校准确定每个姿势的CSAI和剩余的SAI之间的视差映射。此外，建立新开发的基于物理的深度失真模型，以校正深度场的严重失真。为了实现形状和变形测量，通过Lytro Illum捕获其表面上使用散斑图案的测试样品的原始图像，并提取到Sais阵列。使用基于子集的数字图像相关来获得CSAI和目标SAI之间的视差映射。基于预计算的内在和深度参数和视差图，最终确定测试对象的全场表面3D形状和位移。所提出的方法的有效性和准确性通过一组实验来评估涉及圆柱形的形状重建，平面内和平面外位移测量的平板和3D全场位移测量的悬臂梁的实验。初步结果表明，该方法预计将成为全场表面3D形状和位移测量的新方法。

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