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Related Experiment Video

Updated: Jun 28, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond.

Yakun Ju, Kin-Man Lam, Wuyuan Xie

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 12, 2024

Summary

This summary is machine-generated.

This review surveys deep learning methods for photometric stereo, focusing on non-Lambertian surfaces. It highlights advanced performance and suggests future research directions for surface normal estimation.

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Last Updated: Jun 28, 2025

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Area of Science:

Computer Vision
Photogrammetry
Machine Learning

Background:

Photometric stereo reconstructs surface normals using varying illumination.
Non-Lambertian reflectance complicates traditional methods.
Deep learning shows promise for handling complex surface properties.

Purpose of the Study:

To comprehensively review deep learning-based calibrated photometric stereo methods.
To analyze these methods based on input, supervision, and architecture.
To summarize performance and identify future research trends.

Main Methods:

Literature review of deep learning photometric stereo techniques.
Analysis of methods for orthographic cameras and directional lights.
Performance summarization on a standard benchmark dataset.

Main Results:

Deep learning methods demonstrate advanced performance in photometric stereo.
Analysis covers input processing, supervision strategies, and network architectures.
The review consolidates current state-of-the-art deep learning approaches.

Conclusions:

Deep learning significantly enhances photometric stereo, especially for non-Lambertian surfaces.
Existing models show strong performance but have limitations.
Future research should address these limitations for improved surface reconstruction.