# Event-based Background-Oriented Schlieren

arXiv: https://arxiv.org/abs/2311.00434 · HTML: https://mlsanity.com/paper/2311.00434/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 65 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2011.06294/) (2020)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy](https://mlsanity.com/paper/2009.08283/) (2020)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Unifying Motion Deblurring and Frame Interpolation with Events](https://mlsanity.com/paper/2203.12178/) (2022)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [Single Image Optical Flow Estimation with an Event Camera](https://mlsanity.com/paper/2004.00347/) (2020)
- [Globally-Optimal Contrast Maximisation for Event Cameras](https://mlsanity.com/paper/2206.05127/) (2022)
- [Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://mlsanity.com/paper/2112.06242/) (2021)
- [Event-based Camera Pose Tracking using a Generative Event Model](https://mlsanity.com/paper/1510.01972/) (2015)
- [Unraveling the paradox of intensity-dependent DVS pixel noise](https://mlsanity.com/paper/2109.08640/) (2021)
- [A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework](https://mlsanity.com/paper/2212.07350/) (2022)
- [Frequency Cam: Imaging Periodic Signals in Real-Time](https://mlsanity.com/paper/2211.00198/) (2022)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras](https://mlsanity.com/paper/2207.07332/) (2022)

## Built on by (most cited first, 11 of 11)

- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [ETAP: Event-based Tracking of Any Point](https://mlsanity.com/paper/2412.00133/) (2024)
- [TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion](https://mlsanity.com/paper/2505.03116/) (2025)
- [MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice](https://mlsanity.com/paper/2409.03358/) (2024)
- [3D Human Scan With A Moving Event Camera](https://mlsanity.com/paper/2404.08504/) (2024)
- [Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras](https://mlsanity.com/paper/2410.06698/) (2024)
- [Neural Ganglion Sensors: Learning Task-specific Event Cameras Inspired by the Neural Circuit of the Human Retina](https://mlsanity.com/paper/2504.13457/) (2025)
- [Geometric-Photometric Event-based 3D Gaussian Ray Tracing](https://mlsanity.com/paper/2512.18640/) (2025)
- [Unbiased Gradient Estimation for Event Binning via Functional Backpropagation](https://mlsanity.com/paper/2602.12590/) (2026)
- [Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics](https://mlsanity.com/paper/2603.25054/) (2026)

## Abstract (arXiv)

Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding. However, conventional frame-based techniques require both high spatial and temporal resolution cameras, which impose bright illumination and expensive computation limitations. Event cameras offer potential advantages (high dynamic range, high temporal resolution, and data efficiency) to overcome such limitations due to their bio-inspired sensing principle. This paper presents a novel technique for perceiving air convection using events and frames by providing the first theoretical analysis that connects event data and schlieren. We formulate the problem as a variational optimization one combining the linearized event generation model with a physically-motivated parameterization that estimates the temporal derivative of the air density. The experiments with accurately aligned frame- and event camera data reveal that the proposed method enables event cameras to obtain on par results with existing frame-based optical flow techniques. Moreover, the proposed method works under dark conditions where frame-based schlieren fails, and also enables slow-motion analysis by leveraging the event camera's advantages. Our work pioneers and opens a new stack of event camera applications, as we publish the source code as well as the first schlieren dataset with high-quality frame and event data. https://github.com/tub-rip/event_based_bos