Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense force estimation, they are limited by camera frame rates, motion blur, and data bandwidth. Event-based optical tactile sensors offer an attractive alternative with microsecond temporal resolution and low motion blur, but existing methods are restricted to predicting only net forces. We introduce the first framework for dense 3D force field reconstruction using event-based optical tactile sensors. Our approach estimates 3D surface displacements from event data and maps them to forces via the inverse Finite Elements Method (iFEM). Shear displacements are recovered through the proposed event-based marker tracking algorithm, while normal displacements are predicted by a convolutional neural network trained on a collected dataset of synchronized force-displacement-event data. Experiments demonstrate accurate reconstruction of physically grounded forces, achieving a mean absolute error of (0.14 N, 0.10 N, 0.93 N) over force ranges up to (4 N, 4 N, 20 N), while operating at an average of 100 Hz. This work constitutes a first step toward enabling dense force feedback for high-frequency control in robotic grasping and dexterous manipulation.

翻译：人类依赖具有高时间分辨率的空间稠密、几何与力感知的触觉反馈来实现灵巧操作。虽然基于视觉的触觉传感器能够实现稠密力估计，但其受限于相机帧率、运动模糊和数据带宽。基于事件的光学触觉传感器凭借微秒级时间分辨率和低运动模糊特性，成为极具吸引力的替代方案，但现有方法仅局限于预测净力。我们首次提出了利用基于事件的光学触觉传感器实现稠密三维力场重建的框架。该方法从事件数据中估计三维表面位移，并通过逆有限元法（iFEM）将其映射为力。剪切位移通过所提出的事件标记追踪算法恢复，而法向位移则由卷积神经网络预测，该网络在同步采集的力-位移-事件数据集上训练。实验结果表明，该方法能够精确重建物理可信的力场，在力范围分别达（4 N, 4 N, 20 N）时，平均绝对误差为（0.14 N, 0.10 N, 0.93 N），且运行频率平均为100 Hz。这项工作为实现机器人抓取与灵巧操作中高频控制所需的稠密力反馈迈出了第一步。