Abstract
Time of Flight (ToF) is a prevalent depth sensing technology in the fields of robotics, medical imaging, and non-destructive testing. Yet, ToF sensing faces challenges from complex ambient conditions making an inverse modelling from the sparse temporal information intractable. This paper highlights the potential of modern super-resolution techniques to learn varying surroundings for a reliable and accurate ToF detection. Unlike existing models, we tailor an architecture for sub-sample precise semi-global signal localization by combining super-resolution with an efficient residual contraction block to balance between fine signal details and large scale contextual information. We consolidate research on ToF by conducting a benchmark comparison against six state-of-the-art methods for which we employ two publicly available datasets. This includes the release of our SToF-Chirp dataset captured by an airborne ultrasound transducer. Results showcase the superior performance of our proposed StofNet in terms of precision, reliability and model complexity. Our code is available at this https URL.
Abstract (translated)
时间反演(ToF)是机器人、医学成像和非破坏性测试等领域中广泛应用的深部探测技术。然而,ToF感知面临复杂的环境挑战,使得传统的逆模型难以实现。这篇论文强调了现代超分辨率技术学习不同环境范围的潜力,以进行可靠和准确的ToF检测。与现有模型不同,我们设计了一个 sub-样本精确半全局信号定位架构,通过结合超分辨率和高效的剩余收缩块,平衡了精细信号细节和大规模上下文信息。我们通过与六个最先进的方法进行基准比较,巩固了ToF研究,其中包括由空中超声波传感器捕获的我们的SToF-Chirp dataset的发布。结果展示了我们提出的StofNet在精度、可靠性和模型复杂性方面的优越性能。我们的代码可在该httpsURL上可用。
URL
https://arxiv.org/abs/2308.12009
