江苏大学学报（自然科学版）2026，Vol.47Issue(2)：182-188,7.DOI:10.3969/j.issn.1671-7775.2026.02.008
基于自适应多曝光的反光工件三维测量方法
Three-dimensional measurement method for reflective workpieces based on adaptive multiple exposures
摘要
Abstract
To solve the issue of point cloud data loss in reflective workpieces,the three-dimensional measurement method based on adaptive multiple exposures was investigated.The binocular structured light measurement system was designed,and the workpiece point cloud was generated by phase retrieval,phase-based stereo matching and three-dimensional point calculation.The initial sequence of exposure times was determined,and the corresponding initial images were acquired to conduct the statistical analysis of overexposure ratio in the initial images.The relationship between initial exposure time sequence and overexposure ratio was constructed for enabling the adaptive calculation of final exposure time sequence for point cloud generation by the predetermined overexposure ratio.By fusing the multiple exposure images,the point cloud of the reflective workpiece was computed.The system accuracy was evaluated using stepped calibration block,and the experiments on aluminum guide plate and aluminum side plate were conducted.The results show that the average planar distance error of the binocular structured light measurement system is 0.126 9 mm.Compared with the single-exposure imaging method,the proposed approach using fixed overexposure ratio can generate more complete point clouds for different aluminum alloy workpieces with the number of valid points increasing by 55％and 42％for aluminum guide plate and aluminum side plate,respectively.The proposed method effectively enhances the completeness of point clouds for reflective workpieces.关键词
高反光表面/面结构光/三维测量/自适应多曝光/过曝比例/相位融合Key words
highly-reflective surface/surface structured light/three-dimensional measurement/adaptive multiple exposures/overexposure ratio/phase fusion分类
信息技术与安全科学引用本文复制引用
王化明,徐轲,郝琳博,沈颖..基于自适应多曝光的反光工件三维测量方法[J].江苏大学学报（自然科学版）,2026,47(2):182-188,7.基金项目
国家自然科学基金资助项目(52202417) （52202417）
