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.
@article{arxiv.2301.02310,
title = {PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images},
author = {Patrick Grady and Jeremy A. Collins and Chengcheng Tang and Christopher D. Twigg and Kunal Aneja and James Hays and Charles C. Kemp},
journal= {arXiv preprint arXiv:2301.02310},
year = {2024}
}