# SynthPix: A lightspeed PIV image generator

arXiv: https://arxiv.org/abs/2512.09664 · HTML: https://mlsanity.com/paper/2512.09664/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [PDEBENCH: An Extensive Benchmark for Scientific Machine Learning](https://mlsanity.com/paper/2210.07182/) (2022)
- [The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning](https://mlsanity.com/paper/2412.00568/) (2024)
- [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016)
- [CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://mlsanity.com/paper/2310.05963/) (2023)
- [Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers](https://mlsanity.com/paper/2508.10480/) (2025)
- [A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded](https://mlsanity.com/paper/2111.00395/) (2021)
- [Privileged observations enable rapid and reliable policy discovery directly in the physical world](https://mlsanity.com/paper/2512.08463/) (2025)
- [Surrogate-based cross-correlation for particle image velocimetry](https://mlsanity.com/paper/2112.05303/) (2021)
- [PIV/BOS Synthetic Image Generation in Variable Density Environments for Error Analysis and Experiment Design](https://mlsanity.com/paper/1812.05902/) (2018)

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

- [Particle Image Velocimetry Refinement via Consensus ADMM for Active Fluid Control](https://mlsanity.com/paper/2512.11695/) (2025)
- [Flow Gym: A framework for the development, benchmarking, training, and deployment of flow-field quantification methods](https://mlsanity.com/paper/2512.20642/) (2025)

## Abstract (arXiv)

We describe SynthPix, a synthetic image generator for Particle Image Velocimetry (PIV) with a focus on performance and parallelism on accelerators, implemented in JAX. SynthPix produces PIV image pairs from prescribed flow fields while exposing a configuration interface aligned with common PIV imaging and acquisition parameters (e.g., seeding density, particle image size, illumination nonuniformity, noise, blur, and timing). In contrast to offline dataset generation workflows, SynthPix is built to stream images on-the-fly directly into learning and benchmarking pipelines, enabling data-hungry methods and closed-loop procedures -- such as adaptive sampling and acquisition/parameter co-design -- without prohibitive storage and input-output costs. We demonstrate that SynthPix is compatible with a broad range of application scenarios, including controlled laboratory experiments and riverine image velocimetry, and supports rapid sweeps over nuisance factors for systematic robustness evaluation. SynthPix is a tool that supports the flow quantification community and in this paper we describe the main ideas behind the software package.