GUSLO: General and Unified Structured Light Optimization
This work solves the problem of generalizable and accurate 3D reconstruction for industrial inspection and cultural heritage digitization, representing a novel method for a known bottleneck.
The paper tackles the problem of structured light 3D reconstruction by addressing limitations in scene-specific calibration and pattern-specific optimization, proposing GUSLO, which improves accuracy and cross-encoding robustness in industrial and cultural scenarios.
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.