VIRTUS-FPP: Virtual Sensor Modeling for Fringe Projection Profilometry in NVIDIA Isaac Sim

2025-05-20 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

VIRTUS-FPP is a novel physics-based virtual sensor modeling framework for Fringe Projection Profilometry (FPP), developed within NVIDIA Isaac Sim. It addresses FPP's real-world constraints like complex calibration requirements, bulky system footprint, and environmental sensitivity by enabling end-to-end simulation from calibration to 3D reconstruction with full mathematical fidelity to structured light principles. The framework conducts comprehensive virtual calibration, validates reconstruction accuracy against ground truth geometry, and models physical FPP systems as digital twins. Experimental results demonstrate VIRTUS-FPP accurately models optical phenomena, achieves sub-millimeter accuracy (0.512 mm radial error for a 50 mm sphere, 99.7% inliers), and offers 3 FPS data acquisition, more than twice the speed of prior approaches. This significantly accelerates FPP system development through rapid virtual prototyping before physical implementation.

Key takeaway

For Computer Vision Engineers developing or deploying Fringe Projection Profilometry systems, VIRTUS-FPP offers a critical pathway to overcome physical hardware limitations. You can leverage this framework to rapidly prototype new FPP configurations, generate extensive, high-fidelity synthetic datasets for machine learning model training, and rigorously test system performance under diverse, challenging environmental conditions, significantly reducing development time and costs before physical implementation.

Key insights

VIRTUS-FPP enables high-fidelity virtual Fringe Projection Profilometry, accelerating development and data generation without physical hardware dependence.

Principles

• Physics-based simulation accurately models complex optical effects.
• Inverse camera model enables precise virtual projector intrinsics.
• Systematic domain randomization improves algorithm robustness.

Method

VIRTUS-FPP uses NVIDIA Isaac Sim's PhysX and OptiX for scene development and RTX-accelerated ray tracing. It employs a custom Python extension, generates asymmetric circular calibration boards, and applies an 18-step phase-shifting technique for calibration and 3D reconstruction.

In practice

• Rapidly prototype FPP system configurations virtually.
• Generate large-scale synthetic datasets with ground truth for ML.
• Test FPP performance under adverse lighting and material conditions.

Topics

• Fringe Projection Profilometry
• NVIDIA Isaac Sim
• Virtual Sensor Modeling
• 3D Reconstruction
• Digital Twin
• Synthetic Data Generation
• Optical Metrology

Best for: Research Scientist, AI Scientist, Robotics Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.