基于原子力显微镜的微/纳米结构多视图神经三维重建
Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy
AI总结：
针对AFM扫描存在形貌捕获不全和针尖卷积伪影的问题，提出MVN-AFM多视图神经框架，通过迭代同时对齐数据并去伪影，实现低成本、高精度的微/纳米结构三维重建。
摘要：
原子力显微镜（Atomic Force Microscopy，AFM）是一种被广泛用于微/纳米尺度形貌成像的工具。然而，由于样品形貌捕获不完整以及针尖-样品卷积伪影等限制，传统AFM扫描难以精确重建复杂的三维微/纳米结构。在此，我们提出一种基于多视图神经网络的AFM框架（MVN-AFM），可精确重建复杂微/纳米结构的表面模型。与以往工作不同，MVN-AFM不依赖任何特殊形状的探针，也不需要对AFM系统进行昂贵改造。为实现这一目标，MVN-AFM独特地采用迭代方法，同时对齐多视图数据并消除AFM伪影。此外，我们率先将神经隐式曲面重建应用于纳米技术，并取得显著提升的结果。大量实验表明，MVN-AFM可有效消除原始AFM图像中存在的伪影，并重建多种微/纳米结构，包括通过双光子光刻打印的复杂几何微结构，以及PMMA纳米球和ZIF-67纳米晶体等纳米颗粒。本工作为微/纳米尺度三维分析提供了一种高性价比工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
