Neural Field-Based 3D Surface Reconstruction of Microstructures from Multi-Detector Signals in Scanning Electron Microscopy
This addresses the challenge of accurately reconstructing complex 3D microstructures in scientific and industrial SEM applications, representing a strong domain-specific advancement.
The paper tackles the problem of 3D surface reconstruction of microstructures from SEM images by introducing NFH-SEM, a neural field-based hybrid method that eliminates manual calibration and disentangles shadows, resulting in high-fidelity reconstructions of diverse samples like microstructures, pollen, and particle surfaces.
The scanning electron microscope (SEM) is a widely used imaging device in scientific research and industrial applications. Conventional two-dimensional (2D) SEM images do not directly reveal the three-dimensional (3D) topography of micro samples, motivating the development of SEM 3D surface reconstruction methods. However, reconstruction of complex microstructures remains challenging for existing methods due to the limitations of discrete 3D representations, the need for calibration with reference samples, and shadow-induced gradient errors. Here, we introduce NFH-SEM, a neural field-based hybrid SEM 3D reconstruction method that takes multi-view, multi-detector 2D SEM images as input and fuses geometric and photometric information into a continuous neural field representation. NFH-SEM eliminates the manual calibration procedures through end-to-end self-calibration and automatically disentangles shadows from SEM images during training, enabling accurate reconstruction of intricate microstructures. We validate the effectiveness of NFH-SEM on real and simulated datasets. Our experiments show high-fidelity reconstructions of diverse, challenging samples, including two-photon lithography microstructures, peach pollen, and silicon carbide particle surfaces, demonstrating precise detail and broad applicability.