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

arXiv: https://arxiv.org/abs/2509.22685 · HTML: https://mlsanity.com/paper/2509.22685/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 78 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [A Large Dataset of Object Scans](https://mlsanity.com/paper/1602.02481/) (2016)
- [Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation](https://mlsanity.com/paper/2307.05263/) (2023)
- [OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework](https://mlsanity.com/paper/2503.01074/) (2025)
- [Semi-Supervised Video Inpainting with Cycle Consistency Constraints](https://mlsanity.com/paper/2208.06807/) (2022)
- [Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation](https://mlsanity.com/paper/2407.12449/) (2024)
- [Combined Physics and Event Camera Simulator for Slip Detection](https://mlsanity.com/paper/2503.04838/) (2025)

## Built on by (most cited first, 2 of 2)

- [Comprehensive Machine Learning Benchmarking for Fringe Projection Profilometry with Photorealistic Synthetic Data](https://mlsanity.com/paper/2601.08900/) (2026)
- [Repairing Shape-Prior Shortcuts in Long-Range Single-Shot Fringe Projection Profilometry](https://mlsanity.com/paper/2607.11928/) (2026)

## Abstract (arXiv)

Fringe projection profilometry (FPP) is a high-precision structured-light sensing technique for 3D surface reconstruction, yet its practical deployment is often constrained by complex calibration procedures, sensitivity to environmental conditions, and the high cost of physical experimentation. At the same time, robotics research increasingly relies on simulation platforms such as NVIDIA Isaac Sim for scalable development and validation, but accurate virtual representations of optical metrology sensors such as FPP are not currently available. In this work, we present VIRTUS-FPP, the first end-to-end virtual sensor modeling framework for fringe projection profilometry implemented in NVIDIA Isaac Sim, enabling physically grounded simulation of the complete FPP pipeline, including structured light projection, image formation, calibration, and 3D reconstruction, without dependence on pre-calibrated physical systems. The framework leverages an inverse camera model for projector representation, ensuring geometric and photometric fidelity consistent with structured-light principles. By bridging optical metrology and robotics simulation, VIRTUS-FPP enables high-fidelity synthetic data generation, systematic evaluation of sensing pipelines, and digital twin replication of real-world FPP systems. Experimental results demonstrate sub-millimeter reconstruction accuracy and strong correspondence between simulated and physical measurements, highlighting the framework's effectiveness and its potential to advance perception-driven robotics, simulation-to-reality transfer, and scalable optical sensor design.