Particle Image Velocimetry Refinement via Consensus ADMM

• URL: http://arxiv.org/abs/2512.11695v1
• Date: Fri, 12 Dec 2025 16:20:04 GMT
• Title: Particle Image Velocimetry Refinement via Consensus ADMM
• Authors: Alan Bonomi, Francesco Banelli, Antonio Terpin,
• Abstract summary: Particle Image Velocimetry (PIV) is an imaging technique that quantifies flow fields around bluff bodies by analyzing the displacement of neutrally buoyant tracer particles immersed in the fluid.<n>Traditional PIV approaches typically depend on tuning parameters specific to the imaging setup, making the performance sensitive to variations in illumination, flow conditions, and seeding density.<n>In this work, we parallelize the instantaneous flow quantification with multiple algorithms and adopt a consensus framework based on the alternating direction method of multipliers.
• Score: 1.4268442841833953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Particle Image Velocimetry (PIV) is an imaging technique in experimental fluid dynamics that quantifies flow fields around bluff bodies by analyzing the displacement of neutrally buoyant tracer particles immersed in the fluid. Traditional PIV approaches typically depend on tuning parameters specific to the imaging setup, making the performance sensitive to variations in illumination, flow conditions, and seeding density. On the other hand, even state-of-the-art machine learning methods for flow quantification are fragile outside their training set. In our experiments, we observed that flow quantification would improve if different tunings (or algorithms) were applied to different regions of the same image pair. In this work, we parallelize the instantaneous flow quantification with multiple algorithms and adopt a consensus framework based on the alternating direction method of multipliers, seamlessly incorporating priors such as smoothness and incompressibility. We perform several numerical experiments to demonstrate the benefits of this approach. For instance, we achieve a decrease in end-point-error of up to 20% of a dense-inverse-search estimator at an inference rate of 60Hz, and we show how this performance boost can be increased further with outlier rejection. Our method is implemented in JAX, effectively exploiting hardware acceleration, and integrated in Flow Gym, enabling (i) reproducible comparisons with the state-of-the-art, (ii) testing different base algorithms, (iii) straightforward deployment for active fluids control applications.

Related papers

• Purrception: Variational Flow Matching for Vector-Quantized Image Generation [79.74708247230218]
  Purrception is a variational flow matching approach for vector-quantized image generation.<n>Our method adapts Variational Flow Matching to vector-quantized latents by learning categorical posteriors over codebook indices.<n>This combines the geometric awareness of continuous methods with the discrete supervision of categorical approaches.
  arXiv  Detail & Related papers  (2025-10-01T21:41:30Z)
• Score Distillation of Flow Matching Models [67.86066177182046]
  We extend Score identity Distillation (SiD) to pretrained text-to-image flow-matching models.<n>SiD works out of the box across these models, in both data-free and data-aided settings.<n>This provides the first systematic evidence that score distillation applies broadly to text-to-image flow matching models.
  arXiv  Detail & Related papers  (2025-09-29T17:45:48Z)
• Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Cameras [84.58138852280813]
  This study investigates the tremendous potential of spike cameras (a type of ultra-high-speed, high-dynamic-range camera) in Particle Image Velocimetry (PIV)<n>We propose a deep learning framework, Spike Imaging Velocimetry (SIV), designed specifically for highly turbulent and intricate flow fields.<n>We present a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which provides labeled data for three challenging fluid dynamics scenarios.
  arXiv  Detail & Related papers  (2025-04-26T09:14:45Z)
• Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids [0.0]
  This work presents a novel approach for estimating fluid flow fields using neural networks and the optical flow equation.<n>The methodology was validated on synthetic and experimental images.
  arXiv  Detail & Related papers  (2025-01-28T20:40:15Z)
• Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method [60.88467353578118]
  We show that a fixed-point-inspired iterative approach to invert real-world images does not achieve convergence, instead oscillating between distinct clusters. We introduce a simple and fast distribution transfer technique that facilitates image enhancement, stroke-based recoloring, as well as visual prompt-guided image editing.
  arXiv  Detail & Related papers  (2024-11-17T17:45:37Z)
• FlowIE: Efficient Image Enhancement via Rectified Flow [71.6345505427213]
  FlowIE is a flow-based framework that estimates straight-line paths from an elementary distribution to high-quality images. Our contributions are rigorously validated through comprehensive experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-06-01T17:29:29Z)
• Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics [2.0722018747867863]
  We evaluate the ability of optical flow to quantify the spontaneous flows of MT-based active nematics under different labeling conditions. DLOF produces significantly more accurate velocity fields than PIV for densely labeled samples. Our work establishes DLOF as a versatile tool for measuring fluid flows in a broad class of active, soft, and biophysical systems.
  arXiv  Detail & Related papers  (2024-04-23T20:20:00Z)
• FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation [8.78717459496649]
  We propose FlowDepth, where a Dynamic Motion Flow Module (DMFM) decouples the optical flow by a mechanism-based approach and warps the dynamic regions thus solving the mismatch problem. For the unfairness of photometric errors caused by high-freq and low-texture regions, we use Depth-Cue-Aware Blur (DCABlur) and Cost-Volume sparsity loss respectively at the input and the loss level to solve the problem.
  arXiv  Detail & Related papers  (2024-03-28T10:31:23Z)
• MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network [65.1004435124796]
  We propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework. Our experiments demonstrate that the proposed MB-RACS method surpasses current leading methods.
  arXiv  Detail & Related papers  (2024-01-19T04:40:20Z)
• Unsupervised Motion Representation Enhanced Network for Action Recognition [4.42249337449125]
  Motion representation between consecutive frames has proven to have great promotion to video understanding. TV-L1 method, an effective optical flow solver, is time-consuming and expensive in storage for caching the extracted optical flow. We propose UF-TSN, a novel end-to-end action recognition approach enhanced with an embedded lightweight unsupervised optical flow estimator.
  arXiv  Detail & Related papers  (2021-03-05T04:14:32Z)
• Neural Particle Image Velocimetry [4.416484585765027]
  We introduce a convolutional neural network adapted to the problem, namely Volumetric Correspondence Network (VCN) The network is thoroughly trained and tested on a dataset containing both synthetic and real flow data. Our analysis indicates that the proposed approach provides improved efficiency also keeping accuracy on par with other state-of-the-art methods in the field.
  arXiv  Detail & Related papers  (2021-01-28T12:03:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.