Event-Based Method for High-Speed 3D Deformation Measurement under Extreme Illumination Conditions

Banglei Guan; Yifei Bian; Zibin Liu; Haoyang Li; Xuanyu Bai; Taihang Lei; Bin Li; Yang Shang

cs.CVMar 30, 2026

Event-Based Method for High-Speed 3D Deformation Measurement under Extreme Illumination Conditions

Authors: Banglei Guan, Yifei Bian, Zibin Liu, Haoyang Li, Xuanyu Bai, Taihang Lei, Bin Li, Yang Shang,

Organizations: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China. · Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha 410073, China.

Abstract

Background: Large engineering structures, such as space launch towers and suspension bridges, are subjected to extreme forces that cause high-speed 3D deformation and compromise safety. These structures typically operate under extreme illumination conditions. Traditional cameras often struggle to handle strong light intensity, leading to overexposure due to their limited dynamic range. Objective: Event cameras have emerged as a compelling alternative to traditional cameras in high dynamic range and low-latency applications. This paper presents an integrated method, from calibration to measurement, using a multi-event camera array for high-speed 3D deformation monitoring of structures in extreme illumination conditions. Methods: Firstly, the proposed method combines the characteristics of the asynchronous event stream and temporal correlation analysis to extract the corresponding marker center point. Subsequently, the method achieves rapid calibration by solving the Kruppa equations in conjunction with a parameter optimization framework. Finally, by employing a unified coordinate transformation and linear intersection, the method enables the measurement of 3D deformation of the target structure. Results: Experiments confirmed that the relative measurement error is below 0.08%. Field experiments under extreme illumination conditions, including self-calibration of a multi-event camera array and 3D deformation measurement, verified the performance of the proposed method. Conclusions: This paper addressed the critical limitation of traditional cameras in measuring high-speed 3D deformations under extreme illumination conditions. The experimental results demonstrate that, compared to other methods, the proposed method can accurately measure 3D deformations of structures under harsh lighting conditions, and the relative error of the measured deformation is less than 0.1%.

Figures & tables

Figure 1: Flow diagram of the event-based measurement method for high-speed 3D deformation under extreme illumination conditions.

Figure 2: Corresponding points extraction from event streams generated by various event cameras.

Input	Corresponding points for each camera $\mathbf{P}$ , Iteration count $i$ , Initial value of camera projection matrix $\mathbf{M}$
Output	Intrinsic parameters $\mathbf{K}$ , extrinsic parameters $\mathbf{R,T}$
1	Calculate scale factor
2	Optimize the projection structure using the bundle adjustment method
3	Decompose the measurement matrix to obtain the projection reconstruction of the camera matrix and structure matrix
4	Perform Euclidean reconstruction to obtain camera parameters
5	By calculating the reprojection errors of polar geometry and dynamic landmark points, outlier projection points are eliminated

Table 1: Calibration algorithm.

Figure 3: Reference coordinate system transformation.

Panel of Figure 4 — Figure 4: Experimental configuration.

Extrinsics	Event camera 1	Event camera 2	Event camera 3
$(f_{x},f_{y})$	(1778.5077, 1772.3397)	(1756.2173, 1757.6988)	(1816.1340, 1809.6153)
$(C_{x},C_{y})$	(639.5, 359.5)	(639.5, 359.5)	(639.5, 359.5)
$(k_{1},k_{2})$	(-0.05359, 0.33899)	(-0.09119, 0.38824)	(-0.11437, 0.91938)
$(p_{1},p_{2})$	(-0.00157, -0.00479)	(0.00149, -0.00318)	(-0.00144, 0.00497)

Table 2: Calibration results of each camera intrinsics.

Extrinsics	Event camera 2	Event camera 1	Event camera 3
$R$	$\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}$	$\begin{bmatrix}0.99267&0.00302&0.06502\\ 0.01514&0.86947&-0.50846\\ -0.09079&0.51537&0.85804\end{bmatrix}$	$\begin{bmatrix}0.97156&0.09647&0.02984\\ 0.00481&0.29765&-0.93251\\ -0.11474&0.95562&0.30323\end{bmatrix}$
$T$	$\begin{bmatrix}0\\ 0\\ 0\end{bmatrix}$	$\begin{bmatrix}0.2224\\ -0.8944\\ -0.3881\end{bmatrix}$	$\begin{bmatrix}0.1517\\ -0.5671\\ -0.8096\end{bmatrix}$

Table 3: Calibration results of each camera extrinsics.

Figure 5: Mean and standard deviation of reprojection error for each event camera.

Figure 6: Reprojection error and 3D reconstruction results of inliers and camera positions.

Figure 7: Actual distance measurement results of corner points.

Actual distance (mm)	Camera 1 and Camera 2 intersect (mm)	Absolute error (mm)	Relative error
150	149.39	0.61	0.41%
200	201.76	1.76	0.88%
250	245.20	4.80	1.90%
300	299.47	0.53	0.18%

Table 4: Actual distance measurement results of camera 1 and camera 2.

Actual distance (mm)	Camera 1 and Camera 2 intersect (mm)	Absolute error (mm)	Relative error
150	150.31	0.31	0.21%
200	201.69	1.69	0.84%
250	251.32	1.32	0.53%
300	302.32	2.32	0.77%

Table 5: Actual distance measurement results of camera 2 and camera 3.

Panel of Figure 8 — Figure 8: Imaging experiment under extreme illumination conditions.

Panel of Figure 9 — Figure 9: Deformation measurement results.

Structural Health Monitoring

Structural Health Monitoring uses sensor data and computational models to detect, locate, and assess damage in infrastructure and engineered systems.

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Event-based Visual Deformation Measurement

Yuliang Wu, Wei Zhai, Yuxin Cui, Tiesong Zhao+2

MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China · Fuzhou University

Deformable Image Registration Event-Based Vision

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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.

Abstract

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