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The 3D surface measurement plays a significant role in the industrial area. With the advantages of high resolution and acquisition rate, dual line-scan cameras system have been gradually studied for dynamic 3D measurement. However, the partial overexposure and the limited measurement depth range of dual line-scan cameras are the two main elements affecting the quality of the point cloud. Both of these problems are caused by incorrect pixel matching. Therefore, this paper present a matching strategy for dual line-scan cameras based on the one-dimensional background-normalized Fourier transform (1DBNFT) to expands the depth range of measurement and deal with the partial overexposure. The wrapped phases extracted by 1DBNFT, the unwrapped phase extracted by projection distance minimization (PDM), and the matching principle are described in detail. We also analyze the possible errors in the system from three aspects, that are noncoplanar error, installation error, and motion error. Finally, the comparative experiments and accuracy verification experiments demonstrate the effectiveness of our algorithm. IndexTerms-Line-scan cameras, one dimensional background-normalized Fourier transform algorithm, coplanar disparity model, error analysis, point cloud.

Efficient and refined three-dimensional (3D) reconstruction of industrial parts has become an urgent need in the field of advanced manufacturing, and it’s a great challenge when facing in-motion and online inspection requirements of high dynamic range (HDR) surfaces that have large reflectivity variations. This paper proposes a method using RGB line-scan cameras to realize in-motion multiple-shot 3D shape measurements with RGB channel fusion to increase the measurement dynamic range. First, multi-channel one-dimensional background-normalized Fourier transform profilometry (MC-1DBNFTP) is proposed as an effective in-motion HDR method. Second, for HDR surfaces with strongly overexposed areas, we propose a solution that obtains 6 results of different dynamic ranges for fusion with only 5 projected patterns, which further extends the measurement dynamic range while ensuring the small projection period. Third, we develop a fusion method based on reliability evaluation, which is more reliable than the existing methods in fringe projection systems. In addition, colored textures can be mapped to the reconstructed surfaces. Experimental results prove that the proposed method realizes accurate and reliable in-motion 3D reconstruction of HDR surfaces.

Generating a large-scale high-density point cloud is crucial for three-dimensional shape measurements. Both areascan and line-scan camera systems have advantages and limitations for large-scale structured light measurement. Consequently, we propose a method that achieves complementary advantages of both the camera systems through flexible continuous scanning with high resolution and frequency. For point cloud generation, efficient scanning in continuous motion with high resolution, large field of view, and high frequency is realized using line-scan cameras. A flexible registration strategy without mechanical positioning is proposed for point cloud registration after referring to the registration methods in area-scan camera systems. To realize the above functions, system structure and calibration are designed. Techniques for point cloud generation with high dynamic performance and interference resistance are introduced. The point cloud registration strategy includes methods for high-frequency pose acquisition and processing. Moreover, an optimization method is designed for the point cloud after registration, and the texture is attached. The proposed system has excellent prospects for large-scale industrial component measurements.