Automated Exposures Selection for High Dynamic Range Structured-Light 3-D Scanning

Structured light scanning has gained widespread applications in industrial metrology; however, measuring objects that have large surface-reflectivity variations is challenging. For this purpose, the high dynamic range (HDR) technique has become a standard method to fuse features under multiple exposures. Presently, in HDR measurement, manual or cumbersome exposure selection strategies are used. This article reports a new exposure selection approach. An image quality metric is designed to evaluate captured images and suppress regional overexposure, based on which a multiple-exposures selection strategy is developed. Unlike existing methods for single-optimal exposure selection, in our approach, pixel filtering is introduced to locate eligible pixels and candidate pixels in next exposures. Experimental results with industrial parts having large surface-reflectivity variations demonstrated that compared with HDR with 15 exposures (equal time intervals), our proposed approach achieved a similar surface coverage rates (97.4% versus 98.1%) and measurement accuracy (0.043 versus 0.041 mm), but only took 3-4 exposures averagely.

Automated summary

This article presents a new exposure selection approach for high dynamic range measurement. It introduces an image quality metric and pixel filtering to address the challenge of exposure selection in measuring objects with large surface-reflectivity variations. Experimental results showed that compared to traditional HDR methods, the proposed approach achieved similar surface coverage rates and measurement accuracy with only 3-4 exposures on average.