April 15, 2024

Fully optical and three-dimensional object identification.

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Schematic illustration of the all-optical 3D reconstruction and object identification system. (a) An image of the object’s contour surface can be obtained in a single system processing. (b) This all-optical metasurface computing system can reconstruct high- and low-contrast objects.

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A new publication of Optoelectronic advances10.29026/oea.2023.230120 discuss all-optical object identification and three-dimensional reconstruction based on optical computing metasurfaces.

As three-dimensional (3D) reconstruction and object identification techniques become essential in various fields of reverse engineering, artificial intelligence, medical diagnostics, and industrial production, there is an increasing focus on finding much better methods. more efficient, faster and more integrated that can simplify processing. In the current field of object identification and 3D reconstruction, the extraction of sample contour information is mainly achieved by various computer algorithms. Traditional computer processors suffer from multiple limitations, such as high power consumption, low-speed operation, and complex algorithms. In this sense, there has recently been increasing attention in the search for alternative optical methods to perform these techniques. The development of optical computing and image processing theory has provided a more complete theoretical basis for object identification and 3D reconstruction techniques. Optical methods have received more attention as an alternative paradigm than traditional mechanisms in recent years due to their enormous advantages of ultra-fast operation speed, high integration, and low latency. As two-dimensional nanostructures engineered at subwavelength scales, metasurfaces have exhibited remarkable capabilities in revolutionary developments in optics, which can effectively simplify and deeply integrate the footprint of optical systems. In practical applications, metasurfaces have demonstrated the ability to efficiently manipulate various parameters of light. As a result, metasurfaces are used in numerous potential fields, such as analog optical computing, optical cryptography, optical device design, signal manipulation, microscopy imaging, optical imaging, and nanopainting.

As an artificially designed two-dimensional component, the optical computing metasurface has shown the supernatural character of controlling the phase, amplitude, polarization and frequency distributions of the light beam, capable of performing mathematical operations on the input light field. Recently, Professors Hailu Luo’s research group from the School of Physics and Electronics, Hunan University in China proposed an all-optical object identification and 3D reconstruction technique based on optical computing metasurfaces. Unlike traditional mechanisms, this scheme reduces memory consumption in processing contour surface extraction. The identification and reconstruction of experimental results from high- and low-contrast objects agree well with real objects. The exploration of all-optical object identification and 3D reconstruction techniques provides potential applications of high efficiency, low power, and compact systems.

The authors of this paper propose an all-optical 3D reconstruction and object identification technique based on optical computing metasurfaces. By designing and fabricating an optical computing metasurface, all-optical object identification and 3D reconstruction of high- and low-contrast objects are achieved. Different from previous metasurface-based 3D imaging research, this method relies on analog optical computing to obtain the contour information of objects and can achieve object identification and 3D reconstruction of both high and low objects. contrast, which may provide a unique application of analog optical computing based on metasurfaces. The principle of the object identification system is schematically illustrated in Fig. 1(a). When the observed object is added to the system, the system can generate the contour information of the object by the all-optical method. The object identification capability of this system can also be extended to all-optical 3D reconstruction technology. By recombining different projection images of the observed object, a 3D model of the observed object can be obtained, whether it is a high-contrast object or a low-contrast object. [Fig. 1(b)]. In theory, the 3D contour surface of a high-contrast object can be considered as a superposition of infinite two-dimensional contours. Therefore, for high-contrast objects, the rotation method and slicing method are proposed to obtain the 3D reconstruction. For low contrast objects, the 3D reconstruction model can be acquired by breaking the orthogonal polarization state technique.

To confirm the feasibility of 3D reconstruction in the above scheme, a sphere in Fig. 2(a) is taken as an example. By rotating the object at equal intervals in the optical system, the CCD camera can capture multiple contour results of the object in different projection planes, as shown in Fig. 2(b). Finally, the experimental 3D reconstruction model of the high-contrast object can be reconstructed by rearranging and combining all the contour information. [Fig. 2(c)]. In Figs. 3(d)-3(e), coriander seed, mushroom model and paddle model have been used to demonstrate this reconstructed process. In theory, the smaller the separation angle, the more accurate the reconstructed model will be. As proof-of-concept demonstrations, using only the limited contours to illustrate the feasibility of this scheme for 3D reconstruction, the experiment results demonstrate that this technique is facilitative and accurate.

Without loss of generality, the research group focuses on high-contrast objects with complex boundary surfaces. For some high-contrast objects with complex surfaces, the 3D reconstruction method using rotating objects is no longer applicable. Therefore, this group proposed another method of 3D reconstruction by cutting objects. Taking a sphere in Fig. 3(a) as an example, objects are cut at small intervals and a CCD camera can capture multiple contour results of the object in different projection planes, as shown in Fig. 3(b) . Finally, the experimental 3D reconstruction model of the high-contrast object can be reconstructed by rearranging and combining all the contour information. [Fig. 3(c)]. In theory, the higher the precision of the cutting process, the more accurate the reconstructed 3D model will be. As proof-of-concept demonstrations, some simple geometries with distinct features, such as slot, landing, and boss, have been used to verify this experiment in Figs. 3(d1)-3(f1). By cutting these three objects to obtain their contour information in different planes, rearrange and combine those contour information, and finally obtain the 3D experimental reconstruction model about them in Figs. 3(d2)-3(f2). Whether it is a groove with a notch on the inside, a raised protrusion on the outside or a beveled landing, the shapes and sizes of the experimental 3D reconstruction models match the original objects. This method has potential application for 3D reconstruction of objects with complex surfaces or internal structures.

By exploring the application of an all-optical analog computing system based on an optical computing metasurface, an optical object identification and 3D reconstruction technique for high- and low-contrast objects is proposed and realized. This work is expected to be applied to seed screening, surface topography detection, and quantitative 3D microscopic reconstruction. This research will provide a unique direction for image processing and industrial sensing.

Keywords: object identification/three-dimensional reconstruction/optical computing metasurface

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Hailu Luo received a PhD in theoretical physics from Nanjing University, Nanjing, China, in 2007. In 2007, he joined Hunan University, as an assistant professor, and was promoted to professor in 2016. He established the photonics laboratory of spin in 2009. and is currently the head of the spin photonics group. His current research interests include the fundamental theory of spin photonics, analog optical computing, all-optical image processing, quantum measurement, and quantum imaging. The research group has published more than 100 peer-reviewed articles on spin photonics in Physical Review Letters, National Science Review, PNAS, Science Advances, Light: Science & Applications, Reports on Progress in Physics, Opto-Electronic Advances, and Opto-Electronic Advances. Electronic Science. The articles have been cited more than 8,000 times and the H-index has a score of 47 (Google Scholar). He was selected as the most cited Chinese researchers in Elsevier (2020-2022).

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Item reference: Xu DY, Xu WH, Yang Q, Zhang WS, Wen SC et al. All-optical object identification and three-dimensional reconstruction based on optical computing metasurface. Advanced in optoelectrons 6, 230120 (2023). doi: 10.29026/oea.2023.230120


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