Comprehensive Machine Learning Benchmarking for Fringe Projection Profilometry with Photorealistic Synthetic Data

TL;DR

This paper introduces a large, photorealistic synthetic dataset for machine learning in fringe projection profilometry, enabling standardized benchmarking and revealing that current models are limited by information content rather than architecture.

Contribution

It provides the first open-source synthetic dataset for FPP, systematic evaluation of model configurations, and insights into the limitations of single-shot depth prediction from fringe images.

Findings

Individual normalization improves accuracy by 9.1x.

Background fringes are essential, not noise.

UNet performs best among tested architectures.

Abstract

Machine learning approaches for fringe projection profilometry (FPP) are hindered by the lack of large, diverse datasets and standardized benchmarking protocols. This paper introduces the first open-source, photorealistic synthetic dataset for FPP, generated using NVIDIA Isaac Sim, comprising 15,600 fringe images and 300 depth reconstructions across 50 objects. We apply this dataset to single-shot FPP, where models predict 3D depth maps directly from individual fringe images without temporal phase shifting. Through systematic ablation studies, we identify optimal learning configurations for long-range (1.5-2.1 m) depth prediction. We compare three depth normalization strategies and show that individual normalization, which decouples object shape from absolute scale, yields a 9.1x improvement in object reconstruction accuracy over raw depth. We further show that removing background fringe patterns severely degrades performance across all normalizations, demonstrating that background fringes provide essential spatial phase reference rather than noise. We evaluate six loss functions and identify Hybrid L1 loss as optimal. Using the best configuration, we benchmark four architectures and find UNet achieves the strongest performance, though errors remain far above the sub-millimeter accuracy of classical FPP. The small performance gap between architectures indicates that the dominant limitation is information deficit rather than model design: single fringe images lack sufficient information for accurate depth recovery without explicit phase cues. This work provides a standardized benchmark and evidence motivating hybrid approaches combining phase-based FPP with learned refinement. The dataset is available at https://huggingface.co/datasets/aharoon/fpp-ml-bench and code at https://github.com/AnushLak/fpp-ml-bench.