Touch contact and pressure are essential for understanding how humans interact with and manipulate objects, insights which can significantly benefit applications in mixed reality and robotics. However, estimating these interactions from an egocentric camera perspective is challenging, largely due to the lack of comprehensive datasets that provide both accurate hand poses on contacting surfaces and detailed annotations of pressure information. In this paper, we introduce EgoPressure, a novel egocentric dataset that captures detailed touch contact and pressure interactions. EgoPressure provides high-resolution pressure intensity annotations for each contact point and includes accurate hand pose meshes obtained through our proposed multi-view, sequence-based optimization method processing data from an 8-camera capture rig. Our dataset comprises 5 hours of recorded interactions from 21 participants captured simultaneously by one head-mounted and seven stationary Kinect cameras, which acquire RGB images and depth maps at 30 Hz. To support future research and benchmarking, we present several baseline models for estimating applied pressure on external surfaces from RGB images, with and without hand pose information. We further explore the joint estimation of the hand mesh and applied pressure. Our experiments demonstrate that pressure and hand pose are complementary for understanding hand-object interactions. ng of hand-object interactions in AR/VR and robotics research. Project page: \url{https://yiming-zhao.github.io/EgoPressure/}.

翻译：触觉接触与压力对于理解人类如何与物体交互及操控至关重要，这些见解能极大促进混合现实与机器人领域的应用。然而，从第一人称摄像头视角估计此类交互具有挑战性，主要原因是缺乏能同时提供接触表面精确手部姿态及压力信息详细标注的综合性数据集。本文提出EgoPressure——一个捕捉详细触觉接触与压力交互的新型第一人称数据集。EgoPressure为每个接触点提供高分辨率压力强度标注，并通过我们提出的基于多视角序列优化方法处理八相机采集系统的数据，获得精确的手部姿态网格。本数据集包含21名参与者总计5小时的交互记录，通过一台头戴式及七台固定式Kinect相机同步采集，以30Hz频率获取RGB图像与深度图。为支持未来研究与基准测试，我们提出了若干基线模型，用于从RGB图像（无论是否包含手部姿态信息）估计外部表面所受压力。我们进一步探索了手部网格与施加压力的联合估计。实验表明，压力与手部姿态在理解手物交互方面具有互补性，将推动AR/VR与机器人研究中手物交互的理解。项目页面：\url{https://yiming-zhao.github.io/EgoPressure/}。