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Published on: February 23, 2017
Robust Shape-from-Focus via Physics-Inspired Distortion-Aware Focal Depth Regression
1Hubei Key Laboratory of Modern Manufacturing Quantity Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces a new method for microscopic 3D measurement using shape-from-focus (SFF) that corrects for distortions on challenging surfaces. The approach significantly improves accuracy for high dynamic range and low-texture materials.
Area of Science:
- Microscopy and 3D Imaging
- Computer Vision
- Optical Metrology
Background:
- Shape-from-Focus (SFF) is a valuable technique for microscopic 3D measurements.
- High dynamic range (HDR) and weak-textured surfaces introduce focus curve distortions (saturation, spurious peaks, low SNR).
- Existing methods struggle with these distortions due to violated unimodal assumptions, limiting post-processing corrections.
Purpose of the Study:
- To develop a physics-guided, distortion-aware SFF pipeline for opaque single-surface targets.
- To address limitations of traditional SFF on challenging surfaces like HDR and weak-textured materials.
- To enhance the accuracy and reliability of 3D microscopic measurements.
Main Methods:
- Proposed a Distortion-Aware Focal Depth Regression Network (DAFDR-Net) trained on synthetic distortions.
- Implemented Channel-wise Feature Attention (CFA) to reweight distortion-sensitive features.
- Utilized Soft Peak Localization and confidence-guided adaptive smoothing for improved depth estimation.
Main Results:
- Reduced Root Mean Square Error (RMSE) by 36.5% on an HDR free-form surface dataset compared to MRF optimization.
- Compressed the 99th-percentile absolute error from 0.181 to 0.033 on HDR surfaces.
- Decreased flat-region depth standard deviation by 51.3% on weak-textured monocrystalline silicon wafers.
Conclusions:
- The proposed physics-guided SFF pipeline effectively mitigates focus curve distortions.
- DAFDR-Net demonstrates superior performance on challenging surfaces, improving 3D measurement accuracy.
- This method offers a robust solution for microscopic 3D metrology in demanding applications.
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