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Deep Learning for Multi-path Error Removal in ToF Sensors

  • Gianluca Agresti
  • Pietro ZanuttighEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

The removal of Multi-Path Interference (MPI) is one of the major open challenges in depth estimation with Time-of-Flight (ToF) cameras. In this paper we propose a novel method for MPI removal and depth refinement exploiting an ad-hoc deep learning architecture working on data from a multi-frequency ToF camera. In order to estimate the MPI we use a Convolutional Neural Network (CNN) made of two sub-networks: a coarse network analyzing the global structure of the data at a lower resolution and a fine one exploiting the output of the coarse network in order to remove the MPI while preserving the small details. The critical issue of the lack of ToF data with ground truth is solved by training the CNN with synthetic information. Finally, the residual zero-mean error is removed with an adaptive bilateral filter guided from a noise model for the camera. Experimental results prove the effectiveness of the proposed approach on both synthetic and real data.

Keywords

ToF sensors Denoising Multi-path interference Depth acquisition Convolutional Neural Networks 

Notes

Acknowledgment

We would like to thank the Computational Imaging Group at the Sony European Technology Center (EuTEC) for allowing us to use their ToF Explorer simulator and Oliver Erdler, Markus Kamm and Henrik Schaefer for their precious comments and insights. We also thank prof. Calvagno for his support and gratefully acknowledge NVIDIA Corporation for the donation of the GPUs used for this research.

Supplementary material

478822_1_En_30_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (pdf 1118 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PadovaPadovaItaly

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