Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Augmented scene modeling and visualization by optical and acoustic sensor integration.

Andrea Fusiello1, Vittorio Murino

  • 1Dipartimento di Informatica, Universitá degli Studi di Verona, Ca' Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy. fusiello@sci.univr.it

IEEE Transactions on Visualization and Computer Graphics
|November 6, 2004
PubMed
Summary
This summary is machine-generated.

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

An Algebraic Geometry Approach to Viewing Graph Solvability.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer's disease.

NeuroImage·2025
Same author

No Adversaries to Zero-Shot Learning: Distilling an Ensemble of Gaussian Feature Generators.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Computational modeling of human multisensory spatial representation by a neural architecture.

PloS one·2023
Same author

Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable artificial intelligence.

Human brain mapping·2023
Same author

Understanding action concepts from videos and brain activity through subjects' consensus.

Scientific reports·2022
Same journal

Two-phase Impulse Fluid on Particle Flow Map.

IEEE transactions on visualization and computer graphics·2026
Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
See all related articles

This study presents a novel method for underwater scene modeling using multisensor data. The approach integrates acoustic and optical data for enhanced environmental understanding and augmented reality visualization.

Area of Science:

  • Robotics
  • Computer Vision
  • Oceanography

Background:

  • Underwater environments present unique challenges for sensing and modeling due to limited visibility and complex acoustics.
  • Integrating data from multiple sensors (acoustic and optical) is crucial for comprehensive underwater scene understanding.
  • Existing methods may lack the ability to provide intuitive, augmented-reality outputs for inexperienced operators.

Purpose of the Study:

  • To develop an effective underwater scene modeling technique using multisensor data.
  • To create an augmented-reality representation of the underwater environment for easier interpretation.
  • To derive vehicle pose and superimpose model objects onto real-time imagery.

Main Methods:

  • Geometric registration of multisensor acoustic and optical data to a known, a priori geometrical model.

Related Experiment Videos

  • Utilizing data from an underwater vehicle equipped with both acoustic and optical sensing devices.
  • Developing algorithms for accurate vehicle pose estimation and augmented-reality rendering.
  • Main Results:

    • Successful integration of multisensor data for underwater scene modeling.
    • Generation of an augmented-reality output overlaying model objects onto actual underwater images.
    • Demonstrated effectiveness of the approach on a real-world underwater scene.

    Conclusions:

    • The proposed multisensor data integration method enhances underwater scene modeling capabilities.
    • The augmented-reality output significantly improves the understandability of underwater environments for operators.
    • This approach offers a promising solution for real-time underwater exploration and mapping.