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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

FunduScope: a human-centered, machine learning-based interactive tool for training junior ophthalmologists in diabetic retinopathy detection.

Frontiers in big data·2026
Same author

Asynchronous federated learning for web-based OCT image analysis.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

eyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification.

Journal of eye movement research·2025
Same author

I-MPN: inductive message passing network for efficient human-in-the-loop annotation of mobile eye tracking data.

Scientific reports·2025
Same author

A review of machine learning in scanpath analysis for passive gaze-based interaction.

Frontiers in artificial intelligence·2024
Same author

BlinkLinMulT: Transformer-Based Eye Blink Detection.

Journal of imaging·2023

Related Experiment Video

Updated: Jul 27, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.1K

Cross-Viewpoint Semantic Mapping: Integrating Human and Robot Perspectives for Improved 3D Semantic Reconstruction.

László Kopácsi1,2, Benjámin Baffy2, Gábor Baranyi2

  • 1Department of Interactive Machine Learning, German Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, Germany.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a method for robots to create 3D semantic maps understandable by humans, overcoming viewpoint differences using semantic matching and deep learning for improved robot perception and human-robot interaction.

Keywords:
3D semantic mapscomputer visiondeep learninghuman–robot collaborationlabel transferreal-time reconstructionsemantic matchingsemantic segmentationsuperpixel segmentation

More Related Videos

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

782
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

Related Experiment Videos

Last Updated: Jul 27, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.1K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

782
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Allocentric semantic 3D maps are crucial for human-machine interaction, enabling egocentric viewpoint derivation.
  • Discrepancies in class labels and map interpretations arise from differing human and robot perspectives, especially with small robots.
  • Existing 3D semantic reconstruction pipelines struggle with viewpoint variations.

Purpose of the Study:

  • To extend 3D semantic reconstruction pipelines with cross-viewpoint semantic matching for human-robot collaboration.
  • To develop methods for acquiring semantic labels from unusual, low-angle robot viewpoints.
  • To enable small robots to generate high-quality semantic maps for human partners.

Main Methods:

  • Extending a real-time 3D semantic reconstruction pipeline with semantic matching across human and robot viewpoints.
  • Utilizing deep recognition networks and proposing novel approaches for acquiring semantic labels from non-standard perspectives.
  • Adapting human-perspective semantic reconstructions to robot viewpoints using superpixel segmentation and environmental geometry.

Main Results:

  • Achieved high-quality semantic segmentation from the robot's perspective, comparable to human viewpoints.
  • Improved deep network recognition performance for low viewpoints by exploiting gained semantic information.
  • Demonstrated the capability of a small robot to generate high-quality semantic maps autonomously.

Conclusions:

  • The proposed approach effectively bridges the semantic gap between human and robot viewpoints in 3D mapping.
  • Enables small robots to create accurate and useful semantic maps, fostering better human-robot interaction.
  • Real-time performance facilitates interactive applications in robotics and augmented reality.