PressureVision++：从多样化的RGB图像中估计指尖压力
PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images
AI总结：
提出一种仅需RGB相机和配合参与者即可捕获多样化数据的新方法，通过引导施压提示作为弱标签监督模型，构建了51人参与的新数据集，其PressureVision++网络超越人类标注者和先前工作，并成功应用于混合现实触敏界面。
摘要：
触觉在人类抓取与操作中扮演着基础性角色；然而，机器对接触和压力的感知通常需要依赖侵入式传感器。近期研究表明，深度模型能够基于单张RGB图像估计手部压力。但是，由于收集带有真实压力测量值的多样化数据十分困难，相关评估一直局限于受控环境中。我们提出了一种新颖的方法，仅需一台RGB相机和一名配合的参与者即可捕获多样化数据。我们的核心洞察是，可以引导人们以特定方式施加压力，而这种提示可以作为弱标签，监督模型在各种条件下良好运行。我们收集了一个包含51名参与者的新数据集，参与者用指尖接触多样化的物体。我们的网络PressureVision++超越了人类标注者和先前的工作。我们还展示了PressureVision++在混合现实中的应用，其中压力估计使得日常表面能够被用作任意的触敏界面。代码、数据和模型已在线提供。
英文摘要：
Touch plays a fundamental role in manipulation for humans; however, machine perception of contact and pressure typically requires invasive sensors. Recent research has shown that deep models can estimate hand pressure based on a single RGB image. However, evaluations have been limited to controlled settings since collecting diverse data with ground-truth pressure measurements is difficult. We present a novel approach that enables diverse data to be captured with only an RGB camera and a cooperative participant. Our key insight is that people can be prompted to apply pressure in a certain way, and this prompt can serve as a weak label to supervise models to perform well under varied conditions. We collect a novel dataset with 51 participants making fingertip contact with diverse objects. Our network, PressureVision++, outperforms human annotators and prior work. We also demonstrate an application of PressureVision++ to mixed reality where pressure estimation allows everyday surfaces to be used as arbitrary touch-sensitive interfaces. Code, data, and models are available online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
