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Article

Deep Learning for Transient Image Reconstruction from ToF Data

1
Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy
2
R&D Center Europe Stuttgart Laboratory 1, Sony Europe B.V., Hedelfinger Str. 61, 70327 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Thomas Moeslund
Sensors 2021, 21(6), 1962; https://doi.org/10.3390/s21061962
Received: 31 December 2020 / Revised: 10 February 2021 / Accepted: 3 March 2021 / Published: 11 March 2021
(This article belongs to the Special Issue Computer Vision for 3D Perception and Applications)
In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances. View Full-Text
Keywords: Time-of-Flight; multi-path interference; depth estimation; transient imaging; denoising; deep learning Time-of-Flight; multi-path interference; depth estimation; transient imaging; denoising; deep learning
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MDPI and ACS Style

Buratto, E.; Simonetto, A.; Agresti, G.; Schäfer, H.; Zanuttigh, P. Deep Learning for Transient Image Reconstruction from ToF Data. Sensors 2021, 21, 1962. https://doi.org/10.3390/s21061962

AMA Style

Buratto E, Simonetto A, Agresti G, Schäfer H, Zanuttigh P. Deep Learning for Transient Image Reconstruction from ToF Data. Sensors. 2021; 21(6):1962. https://doi.org/10.3390/s21061962

Chicago/Turabian Style

Buratto, Enrico, Adriano Simonetto, Gianluca Agresti, Henrik Schäfer, and Pietro Zanuttigh. 2021. "Deep Learning for Transient Image Reconstruction from ToF Data" Sensors 21, no. 6: 1962. https://doi.org/10.3390/s21061962

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