Neural field hybrid framework reconstructs high-fidelity 3D surfaces from multi-detector SEM images by integrating multi-view geometry, photometric stereo, and a learnable physics-based forward model for self-calibrated, shadow-robust output.
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Neural Field-Based 3D Surface Reconstruction of Microstructures from Multi-Detector Signals in Scanning Electron Microscopy
Neural field hybrid framework reconstructs high-fidelity 3D surfaces from multi-detector SEM images by integrating multi-view geometry, photometric stereo, and a learnable physics-based forward model for self-calibrated, shadow-robust output.