FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry

Aoyu Zhang; Fan Wu; Xiang Feng; Yong Lee

REVIEW 4 major objections 5 minor 25 references

FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that the lack of cross-modal datasets combining frame-based particle images and event-camera streams is the main bottleneck for next-generation particle-based velocimetry, and introduces FED-PV, a generator and 350 GB…

desk verdict Useful new multimodal dataset for particle velocimetry, but the event generation needs to specify its threshold-crossing model before I'd trust the slow-flow subsets. read the letter →

arxiv 2507.06247 v1 pith:MUVBIRPC submitted 2025-07-01 physics.flu-dyn physics.ins-det

Fan Wu , Xiang Feng , Aoyu Zhang , Yong Lee This is my paper · ORCID

classification physics.flu-dynphysics.ins-det

keywords particleimagevelocimetryeventcamerasyntheticdatasetmultimodalfusionflowfieldmeasurementevent-basedimagingdeeplearning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the lack of cross-modal datasets combining frame-based particle images and event-camera recordings is the main bottleneck for next-generation particle-based velocimetry (PV). To fill that gap, it introduces FED-PV, a generator that synthesizes synchronized particle image sequences, event streams, and ground-truth velocity fields from nine types of flow, released as a 350 GB dataset. The paper reports benchmark results with three PIV and three event-based algorithms, showing the dataset can support both conventional and event-driven velocity estimation. If the dataset's synthetic events faithfully represent real event-camera behavior, it would give the community a reusable resource for developing and testing fusion-based PV algorithms.

What carries the argument

The core machinery is the FED-PV generation pipeline, which couples a particle image generator with an event simulator. Particles are modeled as two-dimensional Gaussian intensity profiles and advected through velocity fields using uniform linear motion over $0.02\,\mathrm{ms}$ micro-steps with bilinear interpolation of local velocity. The computational domain is extended from $256\times256$ to $288\times288$ with edge sub-region repopulation to keep particle density stable, and events are produced by comparing log-brightness changes between $100\,\mu\mathrm{s}$ frames against a fixed 25% threshold.

What would settle it

Take a real event camera and record a printed particle field moving at a known constant velocity on a translation stage, then compare the event rate, spatial distribution, and direction of events with FED-PV's generated events for the same nominal flow; if the real event rate versus contrast relationship or the distribution of inter-event times differs substantially from the 25%-threshold model, the synthetic events would not transfer to physical experiments.

Watch

Extended reading notes

Core claim

The central claim of the paper is that FED-PV provides the first large-scale dual-modal dataset for particle-based velocimetry, with synchronized frame and event recordings plus exact ground truth. The dataset is generated by dispersing synthetic Gaussian particles into known CFD flow fields, advecting them with small time steps, and producing events by thresholding log-brightness changes at a 25% contrast threshold. The authors demonstrate the dataset's utility by benchmarking three frame-based PIV methods and three event-based methods, reporting RMSE, AEE, and AAE statistics that show existing algorithms can recover flow structure but still leave room for improvement.

Load-bearing premise

The load-bearing premise is that thresholded log-brightness differences computed from synthetic Gaussian particle images with a fixed 25% contrast threshold and no sensor noise faithfully reproduce what a real event camera would record from a particle-laden flow.

Editorial extensions

If this is right

Researchers can train and evaluate multimodal fusion networks for velocimetry on a common benchmark with known ground truth.
Event-based velocimetry methods can be compared against frame-based PIV baselines under identical flow conditions, enabling head-to-head assessment.
The open-source generator allows custom datasets with different particle densities, thresholds, and velocity fields, supporting domain-specific studies.
The benchmark numbers (RMSE, AEE, AAE) provide reference points that future PV algorithms can be tested against.
The dataset's event streams enable studies of event-camera temporal resolution for flows with velocities from 0.1 to 8.3 pixels/ms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The fixed 25% contrast threshold and noise-free event model may make FED-PV events cleaner than real sensor output; algorithms tuned on this dataset could overfit to noiseless events, so adding realistic sensor noise and threshold variability would be a natural extension.
Because the event stream is derived from the same simulation as the frames, the two modalities are perfectly registered; this makes FED-PV a strong testbed for estimating the value of fusion, but real experiments with imperfect registration may show less benefit.
A direct validation of the event model against a physical event camera on a translating particle pattern would strengthen the claim that the dataset transfers to real experiments.
The dataset's structure could be reused to generate synthetic data for other flow diagnostics, such as predicting pressure fields or doing super-resolution, by adding appropriate ground truth.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my take: FED-PV is a genuinely new resource. No one else has released synchronized frame and event particle data with ground truth for velocimetry, and the generator is customizable to flow type, particle density, and event threshold. That's a real gap, and the paper fills it. The benchmark effort with three PIV and three event-based methods is a reasonable first sanity check; the numbers in Tables 2 and 3 are plausible, and the qualitative flow reconstructions look sensible.

The main soft spot is the event model. Equation (2) says an event fires when |log I(t1) - log I(t2)| >= C, but t2 is never defined. If it's the previous 100-µs frame, then sub-threshold brightness changes never accumulate. For the slower subsets (DNS turbulence, JHTDB channel hd, uniform at the low end), particle displacements can be well under 0.1 px per 100 µs, so you'd get almost no events. That would make the event benchmarks meaningless for exactly the flows where you need them. If, instead, t2 is the time of the last event at that pixel—the standard ESIM approach—then the model is fine, but the paper has to say that and, ideally, verify the implementation against ESIM or a real event camera. The current text is ambiguous, and this is a load-bearing detail.

The other issues are smaller. The frame-timing description is easy to misread: Sec 2.2 says PIV images at 1 ms intervals, Sec 2.3 says an inter-frame interval of 100 µs. I think the 100 µs is the event-generation stream, but the paper should state that explicitly. The metrics (RMSE, AEE, AAE) are never defined. The conclusion claims 'high-fidelity' modeling, but there is no validation against physical event data, so scale that claim back to what was actually done. The GitHub link is given but there is no direct dataset link or access statement.

None of these are fatal. The event-model ambiguity is the one that matters, and it is fixable with a short clarification and a reproducibility check. The paper is worth refereeing, and once the event model is documented and verified, I'd expect this to become a standard reference for anyone working on event-based or fusion-based particle velocimetry.

Referee Report

4 major / 5 minor

Summary. The paper introduces FED-PV, a dual-modal synthetic dataset generator and an associated 350 GB dataset for particle-based velocimetry (PV). The generator advects Gaussian-modeled particles through nine flow-field types taken from Cai et al. (2019), extracts event streams by thresholding log-brightness changes, retains four PIV frames per scenario, and provides ground-truth velocity fields. The paper benchmarks three frame-based PIV methods (UnLiteFlowNet-PIV, Cross-correlation-PIV, RAFT-PIV) and three event-based methods (EBIV, E-RAFT, Contrast-Maximization) to demonstrate usability. The central claim is that FED-PV fills the missing cross-modal frame/event data gap for developing fusion-based PV algorithms.

Significance. If the event-stream generation is faithful to real event-camera behavior, FED-PV is a potentially valuable community resource: it is large-scale, includes ground truth, covers diverse flow types, and ships an open-source generator. The benchmark across six algorithms provides a useful point of reference. The main strengths are the scale of the released dataset and the explicit synthetic-ground-truth design. However, the paper's core value depends on the fidelity of the simulated event data, and that fidelity is currently not established because the event-generation mechanism is under-specified; the benchmark metrics are also undefined. These issues are fixable in revision and do not undermine the overall utility of the dataset if addressed.

major comments (4)

[Sec. 2.3, Eq. (2)] The reference time t2 in Eq. (2) is not defined. If events are generated by comparing only consecutive 100 µs frames, sub-threshold log-intensity increments never accumulate, so slow flows (e.g., the DNS-turbulence and uniform subsets with average velocities as low as 0.1-0.2 px/ms in Table 1) would trigger almost no events even though a real event camera would fire once the accumulated contrast since the last event reaches the threshold. Please specify that the reference is the last-event time at each pixel, and state whether the implementation follows ESIM's threshold-crossing scheme; a comparison of event counts or timestamps against ESIM or a real event recording would substantiate the fidelity claim.
[Secs. 2.2, 2.3 and Figs. 1, 3] The temporal model is internally inconsistent. Section 2.2 states that PIV image sequences are generated at fixed 1 ms intervals and that the last four frames are retained, while Section 2.3 states that the event inter-frame interval is 100 µs and Fig. 3 labels a frame interval as 100 µs. Please clarify whether the event stream is synthesized from a separate high-frame-rate image stream that is not identical to the retained PIV frames, and how the 15 ms event window aligns with the timestamps of the four retained images shown in Fig. 1.
[Secs. 3.1-3.2, Tables 2-3] The evaluation metrics RMSE, AEE, and AAE are never defined. Without formulas and units (particularly for the angular error AAE), the benchmark numbers cannot be reproduced or compared. Please also state over how many scenarios each table entry is averaged and report standard deviations or percentiles, since some rows, such as the uniform-flow row in Table 2, show very large differences among methods that would be informative only with aggregation details.
[Sec. 3.2] The event-based benchmark is not reproducible as described. The paper does not state whether E-RAFT was used with pretrained weights or fine-tuned on the synthetic data, which event representation was fed to the network, or how the grid for Contrast-Maximization was configured. The statement that E-RAFT performs suboptimally 'due to the lack of algorithmic tuning' makes the comparison preliminary. Please report the exact configurations and clarify whether all baseline methods are used strictly off-the-shelf.

minor comments (5)

[Sec. 2.3, Eq. (2)] The log arguments in Eq. (2) are missing the intensity function I; the expression should read |log I(x,y,t1) - log I(x,y,t2)| >= C.
[Sec. 2.3] The sentence explaining positive and negative events is imprecise: a negative event should be generated when the log-brightness decreases by at least C, not when the brightness change is 'below the threshold'.
[Fig. 3] The axes in Fig. 3 are not labeled; the horizontal axis should indicate time with units (ms or µs) and the vertical axis should indicate log-intensity.
[Sec. 1] The contribution bullet 'making it one dual-modal dataset available' reads awkwardly; it should say 'making it the only dual-modal dataset' or 'one of the first dual-modal datasets' as intended.
[Sec. 2.4 / Data availability] The paper provides only a GitHub repository link; please include a persistent dataset DOI or archive link and a clear license statement so that the 350 GB release is reproducible and citable.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: FED-PV is a forward synthetic data generator; benchmarking against its own ground truth is a standard sanity check, not a fitted prediction.

full rationale

FED-PV is not a derivation-from-first-principles paper; it is a data generator. The pipeline starts from published flow fields (Cai et al., 2019) treated as ground truth, places Gaussian particles, advects them by the prescribed velocity, synthesizes grayscale frames, and generates events by thresholding log-brightness changes (Eq. 2, Sec. 2.3). No parameter is fitted to the benchmark outputs, and no quantity is predicted from the same data that defines it. The benchmark sections (3.1, 3.2) run independent PIV and event-based algorithms on the generated images and events, then compare recovered velocity fields to the ground-truth fields used to create the data. This is a self-consistency check of the generator and the algorithms, not a circular prediction; the algorithms are not constructed from the ground truth, and the ground truth is not derived from the algorithms. The references to the authors' prior work (Lee et al., 2017a, 2017b, 2024; Lee and Mei, 2021; Ai et al., 2025a, 2025b) are motivational or technical (e.g., smoothing, PIV limitations), not load-bearing for the dataset's validity, and no uniqueness theorem or ansatz is imported from those works. Concerns about event-camera fidelity, such as the underspecified reference time in Eq. (2) or the absence of sensor noise, are validation and correctness risks, not circularity. The derivation chain is therefore self-contained, and the dataset's usefulness remains an empirical question for external benchmarks rather than a circularly established result.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The construction of FED-PV rests on several modeling choices: flow ground truth borrowed from Cai et al., a Gaussian particle intensity model, a simplified event-generation rule, and a fixed set of numerical parameters (particle density, contrast threshold, time steps). None of these are derived from external constraints; they are engineering choices that shape the dataset's realism. The central utility claim depends on these choices being adequate proxies for real PIV and event-camera physics.

free parameters (6)

particle spatial density = 0.06 particles per pixel
Chosen at initialization; affects image texture and event rate. Stated in Sec 2.2.
event contrast threshold C = 25% brightness change
Empirically selected balance between event density and noise suppression; Sec 2.3.
simulation time step = 0.02 ms
Chosen so particle motion approximates uniform linear movement; Sec 2.2.
event inter-frame interval = 100 µs
Rapid frame rate to keep subpixel particle displacement; Sec 2.3.
number of retained PIV frames = 4
Final four frames at 1 ms intervals are kept; Sec 2.1.
computational domain extension = 288x288
Expanded from 256x256 to mitigate boundary effects; Sec 2.2.

assumptions (5)

domain assumption The flow fields from Cai et al. (2019) provide accurate and representative ground-truth velocity fields for diverse flow types.
Used as the basis for all synthetic scenarios; Sec 2.1.
domain assumption Particle images are adequately represented by 2D Gaussian intensity profiles (Eq. 1) with randomly sampled parameters.
Underpins image synthesis; Sec 2.2.
domain assumption An event camera can be simulated by thresholding log-brightness changes between high-frame-rate images, with linear interpolation between frames.
Event generation model; Sec 2.3, Eq. 2.
standard math Particle motion over a 0.02 ms step is uniform and linear, and velocity is obtained by bilinear interpolation of the local flow field.
Integration scheme used to advance particles; Sec 2.2.
domain assumption The dynamic edge sub-region replenishment preserves a constant particle density and does not bias the flow field.
Used to maintain continuity at domain boundaries; Sec 2.2.

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Cite this review

Pith. "Pith review of FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry." pith.science (2026). https://pith.science/paper/MUVBIRPC

@misc{pith2026250706247,
  author       = {Pith},
  title        = {Pith review of: FED-PV: A Large-Scale Synthetic Frame/Event Dataset for Particle-Based Velocimetry},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MUVBIRPC}},
  note         = {Machine review of arXiv:2507.06247}
}

read the original abstract

Particle-based velocimetry (PV) is a widely used technique for non-invasive flow field measurements in fluid mechanics. Existing PV measurements typically rely on a single type of particle recording. With advancements in deep learning and information fusion, incorporating multiple different particle recordings presents a promising avenue for next-generation PV measurement techniques. However, we argue that the lack of cross-modal datasets -- combining frame-based recordings and event-based recordings -- represents a significant bottleneck in the development of fusion measurement algorithms. To address this critical gap, we developed a dual-modal data generator FED-PV to synthesize frame-based images and event-based recordings of moving particles, resulting in a 350GB dataset generated using our approach. This generator and dataset will facilitate advancements in novel PV algorithms.

Figures

Figures reproduced from arXiv: 2507.06247 by the authors.

**Figure 1.** Timing diagram for FED-PV. This section details the structure of the released dataset and the computational methods used for data generation, with particular emphasis on the technical implementation. Our dataset is based on the widely used dataset in the field of Particle Image Velocimetry (PIV) (Cai et al., 2019), which includes nine distinct flow field types with diverse dynamics. Building upon the work of Cai et … view at source ↗

**Figure 2.** Definition of the computational domain of the flow field. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The Principle of Event Data Generation for the Dataset [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The visualization of our dataset [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: The visualization of PIV Application [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: The visualization of Event Application [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages

[1]

" * write output.state after.block = add.period write newline

ENTRY address author booktitle chapter edition editor eid howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sentence := ...

work page
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

work page
[3]

Physics of Fluids 37

Ai J, Chen Z, Li J, and Lee Y (2025 a ) Rethinking asymmetric image deformation with post-correction for particle image velocimetry. Physics of Fluids 37

work page 2025
[4]

in 16th International Symposium on Particle Image Velocimetry (ISPIV 2025)

Ai J, Chen Z, Ning W, and Lee Y (2025 b ) A projection concentration maximization method for event-based imaging velocimetry. in 16th International Symposium on Particle Image Velocimetry (ISPIV 2025)

work page 2025
[5]

Experiments in Fluids 65:141

AlSattam O, Mongin M, Grose M, Gunasekaran S, and Hirakawa K (2024) Kf-pev: a causal kalman filter-based particle event velocimetry. Experiments in Fluids 65:141

work page 2024
[6]

Experiments in Fluids 60:1--16

Cai S, Zhou S, Xu C, and Gao Q (2019) Dense motion estimation of particle images via a convolutional neural network. Experiments in Fluids 60:1--16

work page 2019
[7]

in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Gallego G, Rebecq H, and Scaramuzza D (2018) A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

work page 2018
[8]

Computational Statistics & Data Analysis 54:1167--1178

Garcia D (2010) Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis 54:1167--1178

work page 2010

Show all 25 references

[9]

IEEE Robotics and Automation Letters 6:4947--4954

Gehrig M, Aarents W, Gehrig D, and Scaramuzza D (2021 a ) Dsec: A stereo event camera dataset for driving scenarios. IEEE Robotics and Automation Letters 6:4947--4954

2021
[10]

in 2021 International Conference on 3D Vision (3DV)

Gehrig M, Millhäusler M, Gehrig D, and Scaramuzza D (2021 b ) E-raft: Dense optical flow from event cameras. in 2021 International Conference on 3D Vision (3DV). pages 197--206

2021
[11]

Experiments in fluids 52:1629--1639

K \"a hler CJ, Scharnowski S, and Cierpka C (2012) On the resolution limit of digital particle image velocimetry. Experiments in fluids 52:1629--1639

2012
[12]

Physics of Fluids 36

Lee Y, Gu F, Gong Z, Pan D, and Zeng W (2024) Surrogate-based cross-correlation for particle image velocimetry. Physics of Fluids 36

2024
[13]

IEEE Transactions on Instrumentation and Measurement 71:1--10

Lee Y and Mei S (2021) Diffeomorphic particle image velocimetry. IEEE Transactions on Instrumentation and Measurement 71:1--10

2021
[14]

Measurement Science and Technology 28:035301

Lee Y, Yang H, and Yin Z (2017 a ) Outlier detection for particle image velocimetry data using a locally estimated noise variance. Measurement Science and Technology 28:035301

2017
[15]

Experiments in Fluids 58:171

Lee Y, Yang H, and Yin Z (2017 b ) Piv-dcnn: cascaded deep convolutional neural networks for particle image velocimetry. Experiments in Fluids 58:171

2017
[16]

Liberzon A, Lasagna D, Aubert M, Bachant P, jakirkham, ranleu, and Borg J (2016) Openpiv/openpiv-python: Updated pyprocess with extended area search method

2016
[17]

The International Journal of Robotics Research 36:142--149

Mueggler E, Rebecq H, Gallego G, Delbruck T, and Scaramuzza D (2017) The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and slam. The International Journal of Robotics Research 36:142--149

2017
[18]

Measurement Science and Technology 11:603

Ohmi K and Li HY (2000) Particle-tracking velocimetry with new algorithms. Measurement Science and Technology 11:603

2000
[19]

springer

Raffel M, Willert CE, Scarano F, K \"a hler CJ, Wereley ST, and Kompenhans J (2018) Particle image velocimetry: a practical guide. springer

2018
[20]

in Conference on robot learning

Rebecq H, Gehrig D, and Scaramuzza D (2018) Esim: an open event camera simulator. in Conference on robot learning. pages 969--982. PMLR

2018
[21]

in Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part II 16

Teed Z and Deng J (2020) Raft: Recurrent all-pairs field transforms for optical flow. in Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part II 16. pages 402--419. Springer

2020
[22]

in Proceedings of the IEEE/CVF International Conference on Computer Vision

Wan Z, Mao Y, Zhang J, and Dai Y (2023) Rpeflow: Multimodal fusion of rgb-pointcloud-event for joint optical flow and scene flow estimation. in Proceedings of the IEEE/CVF International Conference on Computer Vision. pages 10030--10040

2023
[23]

Experiments in Fluids 63:101

Willert CE and Klinner J (2022) Event-based imaging velocimetry: an assessment of event-based cameras for the measurement of fluid flows. Experiments in Fluids 63:101

2022
[24]

in High Performance Computing: ISC High Performance 2020 International Workshops, Frankfurt, Germany, June 21--25, 2020, Revised Selected Papers 35

Zhang M and Piggott MD (2020) Unsupervised learning of particle image velocimetry. in High Performance Computing: ISC High Performance 2020 International Workshops, Frankfurt, Germany, June 21--25, 2020, Revised Selected Papers 35. pages 102--115. Springer

2020
[25]

IEEE Robotics and Automation Letters 3:2032--2039

Zhu AZ, Thakur D, \"O zaslan T, Pfrommer B, Kumar V, and Daniilidis K (2018) The multivehicle stereo event camera dataset: An event camera dataset for 3d perception. IEEE Robotics and Automation Letters 3:2032--2039

2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

" * write output.state after.block = add.period write newline

ENTRY address author booktitle chapter edition editor eid howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid.sentence := ...

work page

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

work page

[3] [3]

Physics of Fluids 37

Ai J, Chen Z, Li J, and Lee Y (2025 a ) Rethinking asymmetric image deformation with post-correction for particle image velocimetry. Physics of Fluids 37

work page 2025

[4] [4]

in 16th International Symposium on Particle Image Velocimetry (ISPIV 2025)

Ai J, Chen Z, Ning W, and Lee Y (2025 b ) A projection concentration maximization method for event-based imaging velocimetry. in 16th International Symposium on Particle Image Velocimetry (ISPIV 2025)

work page 2025

[5] [5]

Experiments in Fluids 65:141

AlSattam O, Mongin M, Grose M, Gunasekaran S, and Hirakawa K (2024) Kf-pev: a causal kalman filter-based particle event velocimetry. Experiments in Fluids 65:141

work page 2024

[6] [6]

Experiments in Fluids 60:1--16

Cai S, Zhou S, Xu C, and Gao Q (2019) Dense motion estimation of particle images via a convolutional neural network. Experiments in Fluids 60:1--16

work page 2019

[7] [7]

in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Gallego G, Rebecq H, and Scaramuzza D (2018) A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

work page 2018

[8] [8]

Computational Statistics & Data Analysis 54:1167--1178

Garcia D (2010) Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis 54:1167--1178

work page 2010