Real-Time Multimodal Fingertip Contact Detection via Depth and Motion Fusion for Vision-Based Human-Computer Interaction

摘要

Precise fingertip contact detection is a fundamental challenge for natural and immersive virtual reality (VR) interaction. However, existing vision-based methods suffer from insufficient accuracy, with typical depth errors (12-25 mm) being too large to reliably distinguish between hovering and true contact (<3 mm). While commercial motion capture systems provide sub-millimeter accuracy, their prohibitive cost limits widespread adoption. This paper addresses this critical gap by developing a highly accurate and cost-effective system for fingertip contact detection. We introduce a novel, specialised dataset of 53,300 RGB-depth pairs capturing millimeter-scale, hand-table typing interactions. By systematically fine-tuning six state-of-the-art depth estimation architectures on this dataset, we reduce the mean absolute error (MAE) by 68%, from 12.3 mm to a state-of-the-art 3.8 mm. Our complete VR keyboard system, TapBoard-X, achieves 95.96% contact detection accuracy and enables typing speeds of 45.6 WPM with a low 3.1% character error rate, rivalling physical keyboards. This performance is achieved at over a 90% cost reduction compared to commercial systems, democratising high-precision hand tracking for the broader research community and paving the way for the next generation of tactile VR experiences.