He Ci, Lai Rong, Sun Jin, Izui Kazuhiro, Wang Zili, Liu Xiaojian, Zhang Shuyou
School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China.
J Imaging. 2025 May 8;11(5):149. doi: 10.3390/jimaging11050149.
This article is used to reconstruct mechanical parts with highly reflective surfaces. Three-dimensional reconstruction based on Phase Measuring Profilometry (PMP) is a key technology in non-contact optical measurement and is widely applied in the intelligent inspection of mechanical components. Due to the high reflectivity of metallic parts, direct utilization of the captured high-dynamic-range images often results in significant information loss in the oversaturated areas and excessive noise in the dark regions, leading to geometric defects and reduced accuracy in the reconstructed point clouds. Many image-fusion-based solutions have been proposed to solve these problems. However, unknown geometric structures and reflection characteristics of mechanical parts lead to the lack of effective guidance for the design of important imaging parameters. Therefore, an adaptive high-precision 3D reconstruction method of highly reflective mechanical parts based on optimization of exposure time and projection intensity is proposed in this article. The projection intensity is optimized to adapt the captured images to the linear dynamic range of the hardware. Image sequence under the obtained optimal intensities is fused using an integration of Genetic Algorithm and Stochastic Adam optimizer to maximize the image information entropy. Then, histogram-based analysis is employed to segment regions with similar reflective properties and determine the optimal exposure time. Experimental validation was carried out on three sets of typical mechanical components with diverse geometric characteristics and varying complexity. Compared with both non-saturated single-exposure techniques and conventional image fusion methods employing fixed attenuation steps, the proposed method reduced the average whisker range of reconstruction error by 51.18% and 25.09%, and decreased the median error by 42.48% and 25.42%, respectively. These experimental results verified the effectiveness and precision performance of the proposed method.
本文用于重建具有高反射表面的机械零件。基于相位测量轮廓术（PMP）的三维重建是非接触式光学测量中的一项关键技术，广泛应用于机械部件的智能检测。由于金属零件的高反射率，直接利用捕获的高动态范围图像通常会导致过饱和区域的大量信息丢失以及暗区的过多噪声，从而导致几何缺陷并降低重建点云的精度。已经提出了许多基于图像融合的解决方案来解决这些问题。然而，机械零件未知的几何结构和反射特性导致在重要成像参数设计方面缺乏有效指导。因此，本文提出了一种基于曝光时间和投影强度优化的高反射机械零件自适应高精度三维重建方法。通过优化投影强度，使捕获的图像适应硬件的线性动态范围。使用遗传算法和随机亚当优化器的集成对在获得的最佳强度下的图像序列进行融合，以最大化图像信息熵。然后，采用基于直方图的分析来分割具有相似反射特性的区域并确定最佳曝光时间。对三组具有不同几何特征和不同复杂度的典型机械部件进行了实验验证。与非饱和单曝光技术和采用固定衰减步长的传统图像融合方法相比，该方法分别将重建误差的平均晶须范围降低了51.18%和25.09%，中位数误差降低了42.48%和25.42%。这些实验结果验证了所提方法的有效性和精度性能。
