Open AccessNoise modelling in time‐of‐flight sensors with application to depth noise removal and uncertainty estimation in three‐dimensional measurement
Author(s) -
Belhedi Amira,
Bartoli Adrien,
Bourgeois Steve,
GayBellile Vincent,
Hamrouni Kamel,
Sayd Patrick
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0135
Subject(s) - noise (video) , noise measurement , gaussian noise , gradient noise , value noise , computer science , repeatability , noise floor , acoustics , artificial intelligence , computer vision , noise reduction , mathematics , statistics , physics , image (mathematics)
Time‐of‐flight (TOF) sensors provide real‐time depth information at high frame‐rates. One issue with TOF sensors is the usual high level of noise (i.e . the depth measure's repeatability within a static setting). However, until now, TOF sensors’ noise has not been well studied. The authors show that the commonly agreed hypothesis that noise depends only on the amplitude information is not valid in practice. They empirically establish that the noise follows a signal‐dependent Gaussian distribution and varies according to pixel position, depth and integration time. They thus consider all these factors to model noise in two new noise models. Both models are evaluated, compared and used in the two following applications: depth noise removal by depth filtering and uncertainty (repeatability) estimation in three‐dimensional measurement.