Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy

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Bibliographic Details
Main Authors:	Chen, Shuo, Peng, Mao, Li, Yijin, Ju, Bing-Feng, Bao, Hujun, Chen, Yuan-Liu, Zhang, Guofeng
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Materials Science
Online Access:	Acceder al recurso
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_version_	1866914647139942400
author	Chen, Shuo Peng, Mao Li, Yijin Ju, Bing-Feng Bao, Hujun Chen, Yuan-Liu Zhang, Guofeng
author_facet	Chen, Shuo Peng, Mao Li, Yijin Ju, Bing-Feng Bao, Hujun Chen, Yuan-Liu Zhang, Guofeng
contents	Atomic Force Microscopy (AFM) is a widely employed tool for micro-/nanoscale topographic imaging. However, conventional AFM scanning struggles to reconstruct complex 3D micro-/nanostructures precisely due to limitations such as incomplete sample topography capturing and tip-sample convolution artifacts. Here, we propose a multi-view neural-network-based framework with AFM (MVN-AFM), which accurately reconstructs surface models of intricate micro-/nanostructures. Unlike previous works, MVN-AFM does not depend on any specially shaped probes or costly modifications to the AFM system. To achieve this, MVN-AFM uniquely employs an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Furthermore, we pioneer the application of neural implicit surface reconstruction in nanotechnology and achieve markedly improved results. Extensive experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro-/nanostructures including complex geometrical microstructures printed via Two-photon Lithography and nanoparticles such as PMMA nanospheres and ZIF-67 nanocrystals. This work presents a cost-effective tool for micro-/nanoscale 3D analysis.
format	Preprint
id	arxiv_https___arxiv_org_abs_2401_11541
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy Chen, Shuo Peng, Mao Li, Yijin Ju, Bing-Feng Bao, Hujun Chen, Yuan-Liu Zhang, Guofeng Computer Vision and Pattern Recognition Materials Science Atomic Force Microscopy (AFM) is a widely employed tool for micro-/nanoscale topographic imaging. However, conventional AFM scanning struggles to reconstruct complex 3D micro-/nanostructures precisely due to limitations such as incomplete sample topography capturing and tip-sample convolution artifacts. Here, we propose a multi-view neural-network-based framework with AFM (MVN-AFM), which accurately reconstructs surface models of intricate micro-/nanostructures. Unlike previous works, MVN-AFM does not depend on any specially shaped probes or costly modifications to the AFM system. To achieve this, MVN-AFM uniquely employs an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Furthermore, we pioneer the application of neural implicit surface reconstruction in nanotechnology and achieve markedly improved results. Extensive experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro-/nanostructures including complex geometrical microstructures printed via Two-photon Lithography and nanoparticles such as PMMA nanospheres and ZIF-67 nanocrystals. This work presents a cost-effective tool for micro-/nanoscale 3D analysis.
title	Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy
topic	Computer Vision and Pattern Recognition Materials Science
url	https://arxiv.org/abs/2401.11541

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