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

Aggregates Classification01:29

Aggregates Classification

359
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
359
Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Unsupervised Domain Adaptive Corner Detection in Vehicle Plate Images.

Sensors (Basel, Switzerland)·2022
Same author

Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition.

Sensors (Basel, Switzerland)·2020
See all related articles

Related Experiment Video

Updated: Aug 10, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K

Point Cloud Classification by Domain Adaptation Using Recycling Max Pooling and Cutting Plane Identification.

Hojin Yoo1, Kyungkoo Jun1,2

  • 1Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Republic of Korea.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study introduces novel unsupervised domain adaptation methods for point cloud classification, improving model robustness and accuracy across different sensor data. The techniques enhance training stability and geometric feature learning for robotics and autonomous driving applications.

Keywords:
point cloud classificationself-paced learningself-supervised learningunsupervised domain adaptation

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Related Experiment Videos

Last Updated: Aug 10, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.5K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Deep models for point cloud classification face challenges due to domain gaps caused by sensor discrepancies.
  • These domain gaps hinder model performance when applied to data from different sources, impacting applications like autonomous driving and robotics.

Purpose of the Study:

  • To propose novel unsupervised domain adaptation schemes to address domain gaps in point cloud classification.
  • To enhance the robustness and accuracy of deep models when applied to point clouds from unfamiliar domains.

Main Methods:

  • A voting-based pseudo-labeling procedure using recycling max pooling and self-paced learning to improve training stability.
  • An unsupervised cutting plane identification method to learn geometrical characteristics of point clouds in novel settings.

Main Results:

  • Achieved an average increase of 6.5% in classification accuracy on benchmark datasets (PointDA-10, Sim-to-Real).
  • Demonstrated improved robustness of domain adaptation through ablation studies, confirming the contribution of each proposed method.

Conclusions:

  • The proposed unsupervised domain adaptation methods effectively reduce domain inconsistency in point cloud classification.
  • The novel schemes enhance model performance and robustness, making deep models more reliable for applications with varying sensor inputs.