用多视角电子显微图像重建微观三维结构，解决阴影与校准难题。
Neural Field-Based 3D Surface Reconstruction of Microstructures from Multi-Detector Signals in Scanning Electron Microscopy
- 基于神经场融合多视角几何与探测器光度信息
- 实现自校准、抗阴影的高保真三维重建
- 适用于材料、生物等微观样本，适合科研人员
微观结构的三维表征对理解与设计功能材料至关重要。然而，广泛应用于科研的扫描电镜（SEM）仅能获取二维电子强度分布。现有三维重建方法在无纹理区域、阴影伪影和校准依赖方面表现不佳，而基于学习的方法因缺乏物理先验和领域特定数据，难以泛化至微观SEM场景。本文提出NFH-SEM，一种基于神经场的混合框架，可从多视角、多探测器的SEM图像中重建高保真三维表面。该方法通过连续神经场融合粗略多视角几何与探测器信号提供的光度立体线索，并引入可学习的前向模型，嵌入扫描电镜成像物理特性，实现自校准、抗阴影的重建。在多种样本上均取得精确恢复，揭示了双光子光刻样品中478 nm的层状结构、花粉颗粒上782 nm的表面纹理以及碳化硅颗粒上的1.559 μm断裂台阶，验证了其准确性和广泛应用潜力。代码与真实数据集已公开于https://github.com/zju3dv/NFH-SEM。
原文摘要 · Abstract (English)
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 $μ$m 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。