Compressive time-of-flight imaging

Antholzer, Stephan; Wolf, Claus; Sandbichler, Michael; Dielacher, Markus; Haltmeier, Markus

doi:10.1109/sampta.2017.8024403

Cited by 1 publication

(8 citation statements)

References 17 publications

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“…The present paper extends the theoretical recovery results stated in [3] from the standard basis to a general sparsifying basis. Additionally, all numerical studies presented in this paper are new and extended significantly compared to [3]. In particular, the TV-regularization studies for the proposed ToF compressed sensing scheme are completely new.…”

Section: Proposed Compressive Tof Imaging Approachsupporting

confidence: 73%

“…Some results of this paper have been presented at the International Conference on Sampling Theory and Applications (SampTA) 2017 in Tallinn [3]. The present paper extends the theoretical recovery results stated in [3] from the standard basis to a general sparsifying basis. Additionally, all numerical studies presented in this paper are new and extended significantly compared to [3].…”

Section: Proposed Compressive Tof Imaging Approachsupporting

confidence: 63%

“…Additionally, the reduction of read-outs allows high spatial and temporal resolution. Some results of this paper have been presented at the International Conference on Sampling Theory and Applications (SampTA) 2017 in Tallinn [3]. The present paper extends the theoretical recovery results stated in [3] from the standard basis to a general sparsifying basis.…”

Section: Proposed Compressive Tof Imaging Approachsupporting

confidence: 63%

“…The generalization to arbitrary Ψ can be shown analogously to the original results. Such a proof is worked out in [2,4]. √ m k R Ω k C v k associated with independent Rademacher vectors v k and subsets Ω k ⊆ {1, .…”

Section: Compressive 3d Sensing Using Block Partial Circulant Matricesmentioning

confidence: 99%

“…As presented in Section 3.1, the depth image is recovered from compressed read-outs by first estimating the differences p (1) − p (3) and p (4) − p (2) from (3.1) and (3.2), which are underdetermined systems of equations of the form y = Mx, and then applying (2.3) to the estimated differences. In this subsection we present how to efficiently solve these underdetermined systems using block-wise 1 -minimization.…”

Section: Image Reconstruction By 1 -Minimizationmentioning

confidence: 99%

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Compressive time-of-flight 3D imaging using block-structured sensing matrices

et al. 2019

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Spatially and temporally highly resolved depth information enables numerous applications including human-machine interaction in gaming or safety functions in the automotive industry. In this paper, we address this issue using Time-of-flight (ToF) 3D cameras which are compact devices providing highly resolved depth information. Practical restrictions often require to reduce the amount of data to be read-out and transmitted. Using standard ToF cameras, this can only be achieved by lowering the spatial or temporal resolution. To overcome such a limitation, we propose a compressive ToF camera design using block-structured sensing matrices that allows to reduce the amount of data while keeping high spatial and temporal resolution. We propose the use of efficient reconstruction algorithms based on 1 -minimization and TV-regularization. The reconstruction methods are applied to data captured by a real ToF camera system and evaluated in terms of reconstruction quality and computational effort. For both, 1 -minimization and TV-regularization, we use a local as well as a global reconstruction strategy. For all considered instances, global TV-regularization turns out to clearly perform best in terms of evaluation metrics including the PSNR.

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Section: Proposed Compressive Tof Imaging Approachsupporting

confidence: 73%