基于神经场的扫描电子显微镜多探测器信号微结构三维表面重建
Neural Field-Based 3D Surface Reconstruction of Microstructures from Multi-Detector Signals in Scanning Electron Microscopy
把神经场与可学习前向模型结合，将 SEM 多探测器信号中的光度立体线索与多视角几何融合，实现自标定、抗阴影的三维表面重建；这套「物理成像模型嵌入神经场」的思路可迁移到其他非标准成像模态（如显微、内窥、X 光）的三维重建。
原文依据 integrates coarse multi-view geometry with photometric stereo cues from detector signals through a continuous neural field
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论微结构的三维表征对于理解和设计功能材料至关重要。然而，科学研究中广泛使用的扫描电子显微镜（SEM）仅能捕获二维电子强度分布。现有的 SEM 三维重建方法在无纹理区域、阴影伪影和标定依赖方面存在困难，而先进的基于学习的方法由于缺乏物理先验和领域特定数据，难以泛化到显微 SEM 领域。我们提出 NFH-SEM，一种基于神经场的混合框架，可从多视角、多探测器 SEM 图像重建高保真三维表面。NFH-SEM 通过连续神经场将粗多视角几何与来自探测器信号的光度立体线索相结合，并引入一个可学习的前向模型，该模型嵌入 SEM 成像物理，以实现自标定、抗阴影的重建。NFH-SEM 在多种样本上实现了精确恢复，在双光子光刻样本中揭示了 478 nm 的分层特征，在花粉颗粒上揭示了 782 nm 的表面纹理，在碳化硅颗粒上揭示了 1.559 mm 的断裂台阶，证明了其准确性和广泛适用性。我们的代码和真实世界数据集可在 https://github.com/zju3dv/NFH-SEM 获取。
英文原文摘要 1286 字符
The 3D characterization of microstructures is crucial for understanding and designing functional materials. However, the scanning electron microscope (SEM), widely used in scientific research, captures only 2D electron intensity distributions. Existing SEM 3D reconstruction methods struggle with textureless regions, shadowing artifacts, and calibration dependencies, whereas advanced learning-based approaches fail to generalize to microscopic SEM domains due to the lack of physical priors and domain-specific data. We introduce NFH-SEM, a neural field-based hybrid framework that reconstructs high-fidelity 3D surfaces from multi-view, multi-detector SEM images. NFH-SEM integrates coarse multi-view geometry with photometric stereo cues from detector signals through a continuous neural field, incorporating a learnable forward model that embeds SEM imaging physics for self-calibrated, shadow-robust reconstruction. NFH-SEM achieves precise recovery across diverse specimens, revealing 478 nm layered features in two-photon lithography samples, 782 nm surface textures on pollen grains, and 1.559 mm fracture steps on silicon carbide particles, demonstrating its accuracy and broad applicability. Our code and real-world dataset are available at https://github.com/zju3dv/NFH-SEM.