Differential Frequency Heterodyne Time-of-Flight Imaging for Instantaneous Depth and Velocity Estimation

被引:0
|
作者
Hu, Yunpu [1 ]
Miyashita, Leo [1 ]
Ishikawa, Masatoshi [1 ]
机构
[1] Univ Tokyo, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1338656, Japan
来源
ACM TRANSACTIONS ON GRAPHICS | 2023年 / 42卷 / 01期
关键词
Heterodyne imaging; velocity sensing; computational time-of-flight imaging; Doppler time-of-flight imaging; correlation function; MODULATION; SENSORS;
D O I
10.1145/3546939
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In this study, we discuss the imaging of depth and velocity using heterodyne-mode time-of-flight (ToF) cameras. In particular, Doppler ToF (D-ToF) imaging utilizes heterodyne modulation to measure the velocity from the Doppler frequency shift, which uniquely facilitates the instantaneous radial velocity estimation. However, theoretical discussion on D-ToF is limited to orthogonal frequency and sinusoidal waveform modulation. This study extends the formulation of the D-ToF imaging, and proposes an arbitrary-frequency, arbitrary-waveform framework considering a phase-compensated, symmetrical two-dimensional correlation map. With the proposed framework, the optimal heterodyne frequency for frequency decoding is found. A differential frequency sampling and decoding method is then proposed, which computes the frequency and phase from as few as four simultaneously captured images. With an experiment platform we built, it is confirmed that the minimum velocity sensing error is half that of the orthogonal frequency method, and the sensible phase range is approximately 2.5 times larger. The conclusions in this study allow the ToF velocity imaging to be applied at the optimal sample frequencies for a wide range of ToF sensors. This pushes one step further to the practical use of ToF velocity imaging.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Differential Frequency Heterodyne Time-of-Flight Imaging for Instantaneous Depth and Velocity Estimation
    Hu, Yunpu
    Miyashita, Leo
    Ishikawa, Masatoshi
    [J]. ACM Transactions on Graphics, 2022, 42 (01):
  • [2] Frequency Based Radial Velocity Estimation in Time-of-Flight Range Imaging
    Lickfold, Carl A.
    Streeter, Lee
    Cree, Michael J.
    Scott, Jonathan B.
    [J]. 2019 INTERNATIONAL CONFERENCE ON IMAGE AND VISION COMPUTING NEW ZEALAND (IVCNZ), 2019,
  • [3] LOW POWER DEPTH ESTIMATION FOR TIME-OF-FLIGHT IMAGING
    Noraky, James
    Sze, Vivienne
    [J]. 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2017, : 2114 - 2118
  • [4] Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
    Noraky, James
    Sze, Vivienne
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 30 (06) : 1524 - 1534
  • [5] DEPTH ESTIMATION OF NON-RIGID OBJECTS FOR TIME-OF-FLIGHT IMAGING
    Noraky, James
    Sze, Vivienne
    [J]. 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2018, : 2925 - 2929
  • [6] Frequency Estimation From Limited Samples: Nonlinearizing Time-of-Flight Radial Velocity Estimation
    Streeter, Lee
    [J]. IEEE SENSORS LETTERS, 2020, 4 (10)
  • [7] Macroscopic Interferometry: Rethinking Depth Estimation with Frequency-Domain Time-of-Flight
    Kadambi, Achuta
    Schiel, Jamie
    Raskar, Ramesh
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 893 - 902
  • [8] Hybrid exposure for depth imaging of a time-of-flight depth sensor
    Shim, Hyunjung
    Lee, Seungkyu
    [J]. OPTICS EXPRESS, 2014, 22 (11): : 13393 - 13402
  • [9] Synthetic Thermal Time-of-Flight(STTOF) depth imaging
    Ringermacher, HI
    Howard, DR
    [J]. REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLS 20A AND 20B, 2001, 557 : 487 - 491
  • [10] Iterative Error Removal for Time-of-Flight Depth Imaging
    Zheng, Zhuolin
    Ding, Yinzhang
    Tang, Xiaotian
    Cai, Yu
    Li, Dongxiao
    Zhang, Ming
    Xie, Hongyang
    Li, Xuanfu
    [J]. ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2021, PT II, 2021, 12892 : 92 - 105