Deep learning for multi-path error removal in tof sensors

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.