Fringe projection profilometry (FPP) is one of the most representative three-dimensional (3D) measurement technology. However, it is difficult to complete accurate 3D measurement with large depth variations at one time, due to the limited depth of field (DOF) of convention lenses. Addressed on this issue, we develop an autofocusing FPP system consisting of a camera with an electrically tunable lens (ETL) and a Micro-Electro-Mechanical System (MEMS) galvanometer laser scanning projector to avoid the limitation of DOF. With this system, we propose a variable focus phase-3D imaging method involving autofocusing phase retrieval and multi-focal length coefficient calibration, to achieve large DOF 3D reconstruction. Autofocus phase retrieval is based on Wiener filter deconvolution algorithm to obtain deblurring fringes under single-frame zoom exposure. To calibrate the phase mapping coefficient, We discretize the continuously variable focal into several in-focus intervals according to the phase modulation function in the measurement depth, and then perform phase-3D mapping coefficient calibration in each focusing interval to finally obtain the multi-focal length mapping coefficient. Experimental results demonstrated that the proposed method can achieve high-efficiency 3D measurement for the depth range of approximately 1,000 mm (300 mm –1300 mm) with the measurement error of 0.05%.
KEYWORDS: Clouds, 3D scanning, Image registration, 3D modeling, Calibration, 3D acquisition, Machine vision, Photogrammetry, Remote sensing, 3D image processing
In this paper, we propose a method of real-time point cloud registration for flexible hand-held 3D scanning. In this study, The problem of point cloud registration to be solved can be divided into refined registration and coarse registration with eight small or large overlap. The fine registration problem is solved by point-to-projection algorithm to ensure high efficiency. In addition, we solve the two types of coarse registration by exhaustive screening with different sampling means. To employ sampling screening algorithm, first we establish multiple matching relationships between two range image by using sampling point pairs, which are derived from the sampling sets of the respective 3D point clouds. Then we propose pose evaluation algorithm(PEA) inspired by ICP to screen out the most optimal matching relationship as the coarse registration result. In this case, we design PEA as a separate kernel function combined with GPU parallel technology to realize real-time computing. Back-projection calibration technology that robust for system distance error solve the problem of pose rejection criteria. The algorithm is highly versatile and robust, since the feature information of the 3D point cloud has never been utilized and extracted. The proposed method has been applied to our hand-held 3D scanners and has been tested on extensive real measured data to demonstrate the effectiveness.
At present, fringe projection profilometry has also been limited with a trade-off between speed and accuracy. For achieving high accuracy measurement, phase-shifting and phase-unwarpping operations will always be used for phase correspondence, however, the phase-unwrapping processing does not contribute to improve the phase accuracy, but just to distinguish phase steps. For futher reducing the projection pattern for phase-unwarpping, we propose a novel method for phase corresponding in bi-cameras system without phase unwrapping. Phase-to-3D mapping structures are utilized to obtain the candidate correspondences and eliminate the ambiguties with wrapped phase, which is implemented efficiently without time-comsuming phase correspondence searching. The experiments on both static and dynamic scenes are perfomed to verify its capability of 120 fps 3D reconstructing speed by overlapped using 3-step phase-shifting pattern.
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