# Event-based Visual Deformation Measurement

arXiv: https://arxiv.org/abs/2602.14376 · HTML: https://mlsanity.com/paper/2602.14376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 16 Feb 2026.

## Builds on (most cited first, 24 of 24)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking](https://mlsanity.com/paper/2307.15055/) (2023)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [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)
- [Dense Continuous-Time Optical Flow from Events and Frames](https://mlsanity.com/paper/2203.13674/) (2022)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [Differentiable Event Stream Simulator for Non-Rigid 3D Tracking](https://mlsanity.com/paper/2104.15139/) (2021)
- [φ-SfT: Shape-from-Template with a Physics-Based Deformation Model](https://mlsanity.com/paper/2203.11938/) (2022)
- [Event-based Non-Rigid Reconstruction from Contours](https://mlsanity.com/paper/2210.06270/) (2022)
- [3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera](https://mlsanity.com/paper/2312.14157/) (2023)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [EF-3DGS: Event-Aided Free-Trajectory 3D Gaussian Splatting](https://mlsanity.com/paper/2410.15392/) (2024)
- [When Deep Learning Meets Digital Image Correlation](https://mlsanity.com/paper/2009.03993/) (2020)
- [Low-power, Continuous Remote Behavioral Localization with Event Cameras](https://mlsanity.com/paper/2312.03799/) (2023)
- [Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End Displacement and Strain Measurement](https://mlsanity.com/paper/2110.13720/) (2021)
- [Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation](https://mlsanity.com/paper/2403.09392/) (2024)
- [Event-Based Motion Magnification](https://mlsanity.com/paper/2402.11957/) (2024)
- [EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation](https://mlsanity.com/paper/2503.11371/) (2025)

## Abstract (arXiv)

Visual Deformation Measurement (VDM) aims to recover dense deformation fields by tracking surface motion from camera observations. Traditional image-based methods rely on minimal inter-frame motion to constrain the correspondence search space, which limits their applicability to highly dynamic scenes or necessitates high-speed cameras at the cost of prohibitive storage and computational overhead. We propose an event-frame fusion framework that exploits events for temporally dense motion cues and frames for spatially dense precise estimation. Revisiting the solid elastic modeling prior, we propose an Affine Invariant Simplicial (AIS) framework. It partitions the deformation field into linearized sub-regions with low-parametric representation, effectively mitigating motion ambiguities arising from sparse and noisy events. To speed up parameter searching and reduce error accumulation, a neighborhood-greedy optimization strategy is introduced, enabling well-converged sub-regions to guide their poorly-converged neighbors, effectively suppress local error accumulation in long-term dense tracking. To evaluate the proposed method, a benchmark dataset with temporally aligned event streams and frames is established, encompassing over 120 sequences spanning diverse deformation scenarios. Experimental results show that our method outperforms the state-of-the-art baseline by 1.6% in survival rate. Remarkably, it achieves this using only 18.9% of the data storage and processing resources of high-speed video methods.