Noise modelling in time‐of‐flight sensors with application to depth noise removal and uncertainty estimation in three‐dimensional measurement. Issue 6 (1st December 2015)

• Record Type: Journal Article
• Authors: Belhedi, Amira; Bartoli, Adrien; Bourgeois, Steve; Gay‐Bellile, Vincent; Hamrouni, Kamel; Sayd, Patrick
• Is Part Of: IET computer vision. Volume 9:Issue 6(2015)
• Page Start: 967
• Page End: 977
• Publication Date: 2015-12-01
• Subjects: Gaussian distribution -- image sensors -- image denoising -- estimation theory -- image filtering -- measurement uncertainty; noise modelling -- time-of-flight sensors -- TOF sensors -- depth noise removal -- uncertainty estimation -- 3D measurement -- signal-dependent Gaussian distribution -- pixel position -- depth filtering -- three-dimensional measurement; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2014.0135
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.