GUSLO统一优化结构光扫描，无需手动调参即可跨场景高精度重建。
GUSLO: General and Unified Structured Light Optimization
- 通过2D三角测量插值实现单次标定，自动生成稠密匹配场。
- 引入显式传输函数补偿伪影，兼顾泛化性与色彩保真度。
- 适用于二值、散斑、彩色编码等多种场景，工业与文保皆适用。
结构光三维重建能精确捕捉物体表面形态，为工业检测和文化遗产数字化提供高精度三维数据。然而现有方法存在两大局限：依赖场景特定标定且需人工调参，以及针对特定结构光模式设计的优化框架，导致跨场景通用性差。本文提出通用统一结构光优化（GUSLO）框架，通过两项协同创新解决上述问题：(1) 基于2D三角测量的单次标定，将稀疏匹配转换为稠密对应场；(2) 通过显式传输函数实现抗伪影光照适应，平衡泛化能力与色彩保真度。我们在二值、散斑和彩色编码等多种设置下进行了广泛实验。结果表明，GUSLO在复杂工业与文化场景中持续优于传统方法，显著提升精度与跨编码鲁棒性。
原文摘要 · Abstract (English)
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.
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