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.
Specific features of the miniature ionisation BSE multi-detector unit for 3D imaging in environmental condi- tions.Micron126, 102752 (2019)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.