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一种用于HDR结构光三维重建的聚类自适应曝光时间选择方法

A Clustered Adaptive Exposure Time Selection Methodology for HDR Structured Light 3D Reconstruction.

作者信息

Li Zhuang, Ma Rui, Duan Shuyu

机构信息

Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.

School of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China.

出版信息

Sensors (Basel). 2025 Aug 3;25(15):4786. doi: 10.3390/s25154786.

DOI:10.3390/s25154786
PMID:40807950
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12349083/
Abstract

Fringe projection profilometry (FPP) has been widely applied in industrial 3D measurement due to its high precision and non-contact advantages. However, FPP often encounters measurement problems with high-dynamic-range objects, consequently impacting phase computation. In this paper, an adaptive exposure time selection method is proposed to calculate the optimal number of exposures and exposure time by using an improved clustering method to divide the region with different reflection degrees. Meanwhile, the phase order sharing strategy is adopted in the phase unwrapping stage, and the same set of complementary Gray code patterns is used to calculate the phase orders under different exposure times. The experimental results demonstrate that the measurement error of the method described in this paper was reduced by 25.4% under almost the same exposure times.

摘要

条纹投影轮廓术(FPP)因其高精度和非接触式优点,已在工业三维测量中得到广泛应用。然而,FPP在测量高动态范围物体时经常遇到问题,从而影响相位计算。本文提出一种自适应曝光时间选择方法,通过使用改进的聚类方法划分不同反射程度的区域,来计算最佳曝光次数和曝光时间。同时,在相位展开阶段采用相位阶数共享策略,使用同一组互补格雷码图案计算不同曝光时间下的相位阶数。实验结果表明,在几乎相同的曝光时间下,本文所述方法的测量误差降低了25.4%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/0111be2acca5/sensors-25-04786-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/7c0a4b22e17b/sensors-25-04786-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/ec7b4c01881a/sensors-25-04786-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/cebf0e7f36ef/sensors-25-04786-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/7b186050f379/sensors-25-04786-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/3d5f13b1d350/sensors-25-04786-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/e2927c2fd876/sensors-25-04786-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/f474619358f4/sensors-25-04786-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/07c6ae0fc3cc/sensors-25-04786-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/0111be2acca5/sensors-25-04786-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/7c0a4b22e17b/sensors-25-04786-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/ec7b4c01881a/sensors-25-04786-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/cebf0e7f36ef/sensors-25-04786-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/7b186050f379/sensors-25-04786-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/3d5f13b1d350/sensors-25-04786-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/e2927c2fd876/sensors-25-04786-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/f474619358f4/sensors-25-04786-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/07c6ae0fc3cc/sensors-25-04786-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6c5/12349083/0111be2acca5/sensors-25-04786-g009.jpg

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