Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Unsupervised Domain Adaptation of Deep Networks for ToF Depth Refinement.

IEEE transactions on pattern analysis and machine intelligence·2021

Same author

A multi-camera dataset for depth estimation in an indoor scenario.

Data in brief·2019

Same author

Model based scattering correction in time-of-flight cameras.

Optics express·2015

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Deep Learning for Transient Image Reconstruction from ToF Data.

Enrico Buratto¹, Adriano Simonetto¹, Gianluca Agresti²

¹Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This summary is machine-generated.

This study introduces a new deep learning method to reduce multi-path interference (MPI) in Time-of-Flight (ToF) cameras. The approach accurately estimates light components, improving depth accuracy by minimizing MPI errors.

Keywords:

Time-of-Flight deep learning denoising depth estimation multi-path interference transient imaging

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Related Experiment Videos

Last Updated: Nov 10, 2025

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

Area of Science:

Computer Vision
Optical Sensing
Machine Learning

Background:

Multi-path interference (MPI) is a significant error source in Time-of-Flight (ToF) cameras.
MPI arises from multiple light reflections, causing depth overestimation in captured images.

Purpose of the Study:

To develop a novel deep learning approach for correcting MPI in ToF cameras.
To accurately estimate direct and global light components for improved depth sensing.

Main Methods:

A deep learning model is proposed to estimate the scene's time-dependent impulse response.
The model comprises a predictive block for encoding backscattering vectors and a fixed block for light response translation.
This approach recovers depth images with substantially reduced MPI.

Main Results:

Experimental validation on real-world data demonstrates the approach's effectiveness.
The proposed method achieves state-of-the-art performance in MPI correction for ToF cameras.
Significant reduction in depth errors caused by MPI was observed.

Conclusions:

The novel deep learning method effectively mitigates multi-path interference in ToF cameras.
Accurate estimation of light components leads to more precise depth measurements.
This work advances the performance of ToF sensing technology.