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.

翻译：视觉形变测量旨在通过从相机观测中追踪表面运动来恢复密集的形变场。传统的基于图像的方法依赖于帧间微小运动来约束对应点搜索空间，这限制了其在高度动态场景中的适用性，或需要以高昂的存储和计算开销为代价采用高速相机。我们提出了一种事件-帧融合框架，利用事件提供时间上稠密的运动线索，并利用帧进行空间上稠密的精确估计。通过重新审视固体弹性建模先验，我们提出了仿射不变单纯形框架。该框架将形变场划分为具有低参数化表示的线性化子区域，有效缓解了由稀疏且含噪声的事件引起的运动模糊性。为了加速参数搜索并减少误差累积，引入了一种邻域贪婪优化策略，使收敛良好的子区域能够指导其收敛较差的相邻区域，从而有效抑制长期密集跟踪中的局部误差累积。为了评估所提出的方法，我们建立了一个包含时间对齐事件流和帧的基准数据集，涵盖超过120个涵盖多种形变场景的序列。实验结果表明，我们的方法在生存率上优于最先进的基线方法1.6%。值得注意的是，它仅使用了高速视频方法18.9%的数据存储和处理资源就实现了这一性能。