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Research Area

Author

  • Enrico Buratto*, Adriano Simonetto*, Gianluca Agresti, Henrik Schäfer, Pietro Zanuttigh*

Company

  • Sony Europe B.V.

Venue

  • MDPI

Date

  • 2021

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Deep Learning for Transient Image Reconstruction from ToF Data

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Abstract

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.

MDPI Sensors

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