GUSLO: General and Unified Structured Light Optimization

TL;DR

GUSLO introduces a unified structured light optimization framework that enhances 3D reconstruction accuracy and robustness across various SL patterns by combining single-shot calibration and artifact-aware photometric adaptation.

Contribution

It presents a novel, generalizable approach to structured light 3D reconstruction that reduces calibration effort and improves cross-encoding robustness across diverse scenarios.

Findings

GUSLO outperforms conventional methods in accuracy.

It demonstrates robustness across binary, speckle, and color-coded settings.

The framework improves performance in industrial and cultural heritage applications.

Abstract

Structured light (SL) 3D reconstruction captures the precise surface shape of objects, providing high-accuracy 3D data essential for industrial inspection and cultural heritage digitization. However, existing methods suffer from two key limitations: reliance on scene-specific calibration with manual parameter tuning, and optimization frameworks tailored to specific SL patterns, limiting their generalizability across varied scenarios. We propose General and Unified Structured Light Optimization (GUSLO), a novel framework addressing these issues through two coordinated innovations: (1) single-shot calibration via 2D triangulation-based interpolation that converts sparse matches into dense correspondence fields, and (2) artifact-aware photometric adaptation via explicit transfer functions, balancing generalization and color fidelity. We conduct diverse experiments covering binary, speckle, and color-coded settings. Results show that GUSLO consistently improves accuracy and cross-encoding robustness over conventional methods in challenging industrial and cultural scenarios.