Near-light Photometric Stereo with Symmetric Lights

2026-03-17

AI Summary

This work addresses the challenges in near-light photometric stereo—specifically, non-convex optimization, reliance on accurate initialization, and precise light source calibration—for shape recovery. The authors propose a novel approach based on symmetric near-light configurations, where multiple pairs of light sources are arranged symmetrically about an arbitrary point. This setup enables the formulation of a linear photometric stereo model that, for the first time, permits closed-form solutions for both surface normals and depth. The method eliminates the need for initial guesses or full light source calibration, substantially reducing dependence on stringent calibration conditions. It is thus applicable to uncalibrated scenarios with spatial offsets and achieves reconstruction accuracy comparable to state-of-the-art calibrated methods—all without requiring careful initialization.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This paper describes a linear solution method for near-light photometric stereo by exploiting symmetric light source arrangements. Unlike conventional non-convex optimization approaches, by arranging multiple sets of symmetric nearby light source pairs, our method derives a closed-form solution for surface normal and depth without requiring initialization. In addition, our method works as long as the light sources are symmetrically distributed about an arbitrary point even when the entire spatial offset is uncalibrated. Experiments showcase the accuracy of shape recovery accuracy of our method, achieving comparable results to the state-of-the-art calibrated near-light photometric stereo method while significantly reducing requirements of careful depth initialization and light calibration.

Problem

• near-light photometric stereo
• symmetric light sources
• surface normal estimation
• depth recovery
• light calibration

Innovation

• near-light photometric stereo
• symmetric light arrangement
• closed-form solution
• uncalibrated setup
• surface normal estimation