使用原始飞行时间观测的300Hz基于模型的跟踪

Jan Stühmer, Sebastian Nowozin, A. Fitzgibbon, R. Szeliski, Travis Perry, S. Acharya, D. Cremers, J. Shotton
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引用次数: 20

摘要

消费者深度相机极大地提高了我们实时跟踪刚性、铰接和可变形3D物体的能力。然而,深度相机具有有限的时间分辨率(帧率),这限制了跟踪的准确性和鲁棒性,特别是对于快速或不可预测的运动。在本文中,我们展示了如何执行基于模型的对象跟踪,该跟踪允许通过简单修改现成的深度相机以更高的帧率重建对象的深度。我们专注于基于相位的飞行时间(ToF)传感,它从一组短曝光“原始”红外捕获中重建每个低帧率深度图像。这些原始捕获是在每个深度帧开始附近快速连续拍摄的,并且在其主动照明的调制方面有所不同。我们有两个贡献。首先,我们详细介绍了如何针对这些原始捕获执行基于模型的跟踪。其次,我们表明,通过重新编程相机,使原始捕获在时间上均匀间隔,我们获得了10倍的高帧率,从而提高了跟踪快速移动物体的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-Based Tracking at 300Hz Using Raw Time-of-Flight Observations
Consumer depth cameras have dramatically improved our ability to track rigid, articulated, and deformable 3D objects in real-time. However, depth cameras have a limited temporal resolution (frame-rate) that restricts the accuracy and robustness of tracking, especially for fast or unpredictable motion. In this paper, we show how to perform model-based object tracking which allows to reconstruct the object's depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera. We focus on phase-based time-of-flight (ToF) sensing, which reconstructs each low frame-rate depth image from a set of short exposure 'raw' infrared captures. These raw captures are taken in quick succession near the beginning of each depth frame, and differ in the modulation of their active illumination. We make two contributions. First, we detail how to perform model-based tracking against these raw captures. Second, we show that by reprogramming the camera to space the raw captures uniformly in time, we obtain a 10x higher frame-rate, and thereby improve the ability to track fast-moving objects.
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