Abstract
Propelled by the rapidly growing demand for function incorporation and performance improvement, various specular components with complex structured surfaces are broadly applied in numerous optical engineering arenas. Form accuracy of the structured surfaces directly impacts the functioning of the specular components. Because the scales of these structures and/or the importance of their functions are usually designed differently, the structures require different measurement demands in scale, lateral resolution, and accuracy. In this paper, a multiscale form measurement technique is proposed based on triple-sensor phase measuring deflectometry for measuring structured specular surfaces. The proposed technique contains two sub-phase measuring deflectometry(PMD)-systems. Each sub-system works as a single segmentation PMD (SPMD) system and is designed to have different measurement scales, lateral resolutions, and accuracies to meet the measurement demands of the targeted surfaces. Two imaging sensors in the proposed technique cover the measured full-scale surface. The specular surface is separated into several continuous segments through algorithms and the spatial relationship of the continuous segments is established based on absolute depth data calculated through the triangular relationship between the two imaging sensors. The third imaging sensor with a long working distance only captures the field of the small-scale structures and reconstructs the structures based on gradient data to improve the structures’ reconstruction resolution and accuracy. In order to make it suitable for portable and embedded measurement, a compact configuration is explored to reduce system volume. Data fusion techniques are also studied to combine the measurement data of the two sub-systems. Experimental results demonstrate the validity of a portable prototype developed based on the proposed technique by measuring a concave mirror with small-scale structures.
Original language | English |
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Article number | 17 |
Number of pages | 12 |
Journal | Visual Intelligence |
Volume | 1 |
Issue number | 1 |
Early online date | 24 Aug 2023 |
DOIs | |
Publication status | Published - 1 Dec 2023 |