Robust three-dimensional(3D)recognition across different viewing angles is crucial for dynamic applications such as autonomous navigation and augmented reality;however,the application of the technology remains challen...Robust three-dimensional(3D)recognition across different viewing angles is crucial for dynamic applications such as autonomous navigation and augmented reality;however,the application of the technology remains challenging owing to factors such as orientation,deformation,and noise.Wave-based analogous computing,particularly diffraction neural networks(DNNs),constitutes a scan-free,energy-efficient means of mitigating these issues with strong resilience to environmental disturbances.Herein,we present a real-time all-directional 3D object recognition and distortion correction system using a deep knowledge prior DNN.Our approach effectively addressed complex two-dimensional(2D)and 3D distortions by optimizing the metasurface parameters with minimal training data and refining them using DNNs.Experimental results demonstrate that the system can effectively rectify distortions and recognize objects in real time,even under varying perspectives and multiple complex distortions.In 3D recognition,the prior DNN reliably identifies both dynamic and static objects,maintaining stable performance despite arbitrary orientation changes,highlighting its adaptability to complex and dynamic environments.Our system can function either as a preprocessing tool for imaging platforms or as a stand-alone solution,facilitating 3D recognition tasks such as motion sensing and facial recognition.It offers a scalable solution for high-speed recognition tasks in dynamic and resource-constrained applications.展开更多
基金supported by the National Key Research and Development Program of China (Grant Nos. 2022YFA1404704, 2022YFA1405200, and 2022YFA1404902)the National Natural Science Foundation of China (NNSFC) (Grant Nos. 61975176 and 62071423)+3 种基金the Key Research and Development Program of Zhejiang Province (Grant Nos. 2022C01036 and 2024C01160)the Natural Science Foundation of Zhejiang Province (Grant No. LR23F010004)the Top-Notch Young Talent of Zhejiang Provincethe Fundamental Research Funds for the Central Universities
文摘Robust three-dimensional(3D)recognition across different viewing angles is crucial for dynamic applications such as autonomous navigation and augmented reality;however,the application of the technology remains challenging owing to factors such as orientation,deformation,and noise.Wave-based analogous computing,particularly diffraction neural networks(DNNs),constitutes a scan-free,energy-efficient means of mitigating these issues with strong resilience to environmental disturbances.Herein,we present a real-time all-directional 3D object recognition and distortion correction system using a deep knowledge prior DNN.Our approach effectively addressed complex two-dimensional(2D)and 3D distortions by optimizing the metasurface parameters with minimal training data and refining them using DNNs.Experimental results demonstrate that the system can effectively rectify distortions and recognize objects in real time,even under varying perspectives and multiple complex distortions.In 3D recognition,the prior DNN reliably identifies both dynamic and static objects,maintaining stable performance despite arbitrary orientation changes,highlighting its adaptability to complex and dynamic environments.Our system can function either as a preprocessing tool for imaging platforms or as a stand-alone solution,facilitating 3D recognition tasks such as motion sensing and facial recognition.It offers a scalable solution for high-speed recognition tasks in dynamic and resource-constrained applications.
