3DSEM: A Dataset for 3D SEM Surface Reconstruction

The <strong>Scanning Electron Microscope (SEM)</strong> as 2D imaging instrument has been widely used in biological, mechanical, and materials sciences to determine the surface attributes (e.g., compositions or geometries) of microscopic specimens. A SEM offers an excellent capability to overcome the limitation of human eyes by achieving increased magnification, contrast, and resolution greater than 1 nanometer. However, SEM micrographs still remain two-dimensional (2D). Having truly three-dimensional (3D) shapes from SEM micrographs would provide anatomic surfaces allowing for quantitative measurements and informative visualization of the objects being investigated. In biology, for example, <strong>3D SEM surface reconstructions</strong> would enable researchers to investigate surface characteristics and recognize roughness, flatness, and waviness of a biological structure. There are also various applications in material and mechanical engineering in which 3D representations of material properties would allow us to accurately measure a fractal dimension and surface roughness and design a micro article which needs to fit into a tiny appliance.

3D SEM surface reconstruction employs several computational technologies, such as multi-view geometry, computer vision, optimization strategies, and machine learning to tackle the inverse problem going from 2D to 3D. In this contribution, an attempt is made to provide a 3D microscopy dataset along with the underlying algorithms publicly and freely available at <strong>http://selibcv.org/3dsem/</strong> for the research community.

扫描电子显微镜（Scanning Electron Microscope, SEM）作为二维成像仪器，已被广泛应用于生物学、机械学与材料科学领域，用于测定微观样本的表面属性（如成分或几何形貌）。扫描电镜可实现更高的放大倍率、对比度与优于1纳米的分辨率，极佳地突破了人眼的观测局限。然而，扫描电镜显微图像仍为二维（2D）形式。若能从这类二维图像中还原出真实的三维（3D）形态，便可获取样本的解剖学表面信息，进而实现对研究对象的定量测量与可视化呈现。以生物学研究为例，三维扫描电镜表面重建（3D SEM surface reconstructions）可帮助研究者探究样本的表面特征，识别生物结构的粗糙度、平整度与波纹度。在材料与机械工程领域也存在诸多相关应用，通过获取材料属性的三维表征，我们能够精准测算分形维数与表面粗糙度，并设计出适配微型设备的微结构部件。
三维扫描电镜表面重建技术依托多视图几何、计算机视觉、优化策略与机器学习等多种计算技术，以解决从二维到三维的逆问题。在本研究中，我们尝试构建一套公开可免费获取的三维显微学数据集及其配套算法，相关资源已发布于http://selibcv.org/3dsem/，供科研社区使用。

搜集汇总

背景与挑战

背景概述

3DSEM数据集是一个用于3D SEM表面重建的公开数据集，包含2D SEM图像和对应的3D点云数据，适用于生物、材料和机械工程等领域的研究。数据集还提供了相关算法，支持从2D图像到3D表面的转换。