# GUSLO: General and Unified Structured Light Optimization

arXiv: https://arxiv.org/abs/2501.14659 · HTML: https://mlsanity.com/paper/2501.14659/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [End-to-end Full Projector Compensation](https://mlsanity.com/paper/2008.00965/) (2020)
- [End-to-end Projector Photometric Compensation](https://mlsanity.com/paper/1904.04335/) (2019)
- [Directionally Decomposing Structured Light for Projector Calibration](https://mlsanity.com/paper/2110.03924/) (2021)

## Abstract (arXiv)

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.