RESUMO
Structured light illumination is widely applied for surface defect detection due to its advantages in terms of speed, precision, and non-contact capabilities. However, the high reflectivity of metal surfaces often results in the loss of point clouds, thus reducing the measurement accuracy. In this paper, we propose a novel quaternary categorization strategy to address the high-reflectivity issue. Firstly, we classify the pixels into four types according to the phase map characteristics. Secondly, we apply tailored optimization and reconstruction strategies to each type of pixel. Finally, we fuse point clouds from multi-type pixels to accomplish precise measurements of high-reflectivity surfaces. Experimental results show that our strategy effectively reduces the high-reflectivity error when measuring metal surfaces and exhibits stronger robustness against noise compared to the conventional method.
RESUMO
In fringe projection profilometry, inevitable distortion of optical lenses decreases phase accuracy and decreases the quality of 3D point clouds. For camera lens distortion, existing compensation methods include real time look-up tables derived from the related parameters of camera calibration. However, for projector lens distortion, so far, post-undistortion methods iteratively correcting lens distortion are relatively time-consuming while, despite avoiding iteration, pre-distortion methods are not suitable for binary fringe patterns. In this paper, we aim to achieve real-time phase correction for the projector by means of a scale-offset model that characterizes projector distortion by four correction parameters within a small-enough area, and thus we can speed up the post-undistortion by looking up tables. Experiments show that the proposed method can suppress the distortion error by a factor of 20 ×, i.e., the error of root mean square is less than 45 µm/0.7, while also proposed improving the computation speed by a factor of 50× over traditional iterative post-undistortion.