Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
Name
_2017__Asilomar__A_Method_to_Estimate_the_Energy_Consumption_of_Deep_Neural_Networks.pdf
Size
1.25 MB
Format
Adobe PDF
Checksum (MD5)
a9d987a47c18f3ab789f40510df68a5b
Author(s)
Noraky, James
Sze, Vivienne
Date Issued
June 2020
Journal
IEEE Transactions on Circuits and Systems for Video Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Noraky, James and Vivienne Sze. "Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging." IEEE Transactions on Circuits and Systems for Video Technology 30, 6 (June 2020): 1524 - 1534. © 2020 IEEE
Version
Author's final manuscript
Abstract
Depth sensing is useful in a variety of applications that range from augmented reality to robotics. Time-of-flight (TOF) cameras are appealing because they obtain dense depth measurements with minimal latency. However, for many battery-powered devices, the illumination source of a TOF camera is power hungry and can limit the battery life of the device. To address this issue, we present an algorithm that lowers the power for depth sensing by reducing the usage of the TOF camera and estimating depth maps using concurrently collected images. Our technique also adaptively controls the TOF camera and enables it when an accurate depth map cannot be estimated. To ensure that the overall system power for depth sensing is reduced, we design our algorithm to run on a low power embedded platform, where it outputs 640 × 480 depth maps at 30 frames per second. We evaluate our approach on several RGB-D datasets, where it produces depth maps with an overall mean relative error of 0.96% and reduces the usage of the TOF camera by 85%. When used with commercial TOF cameras, we estimate that our algorithm can lower the total power for depth sensing by up to 73%.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tcsvt.2019.2907904
