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

Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

485
Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
485
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.2K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.2K
Three-Dimensional Force System01:30

Three-Dimensional Force System

2.7K
In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Efficient Vertical Structure Correlation and Power Line Inference.

Sensors (Basel, Switzerland)·2024
Same author

A Mass-Conservation Model for Stability Analysis and Finite-Time Estimation of Spread of COVID-19.

IEEE transactions on computational social systems·2023
Same author

Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression.

Sensors (Basel, Switzerland)·2023
Same author

Automated Curb Recognition and Negotiation for Robotic Wheelchairs.

Sensors (Basel, Switzerland)·2021
Same author

Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps.

Sensors (Basel, Switzerland)·2021
Same author

Roof Shape Classification from LiDAR and Satellite Image Data Fusion Using Supervised Learning.

Sensors (Basel, Switzerland)·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 10, 2025

Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

511

Polylidar3D-Fast Polygon Extraction from 3D Data.

Jeremy Castagno1, Ella Atkins1

  • 1Robotics Institute, University of Michigan, Ann Arbor, MI 48105, USA.

Sensors (Basel, Switzerland)
|August 30, 2020
PubMed
Summary
This summary is machine-generated.

Polylidar3D efficiently extracts non-convex polygons from 3D point clouds, representing flat surfaces. This method significantly reduces computation for applications like mapping and localization.

Keywords:
LiDARgeometrymappingpoint cloudpolygon

More Related Videos

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
08:32

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

Published on: October 20, 2023

3.7K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

555

Related Experiment Videos

Last Updated: Dec 10, 2025

Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

511
Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
08:32

Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models

Published on: October 20, 2023

3.7K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

555

Area of Science:

  • Computer Vision
  • Computational Geometry
  • Robotics

Background:

  • 3D point clouds are crucial for localization, mapping, and modeling.
  • Processing dense point clouds is computationally intensive.
  • Low-dimensional representations like polygons are desirable for efficiency.

Purpose of the Study:

  • To introduce Polylidar3D, an algorithm for extracting non-convex polygons from 3D point clouds.
  • To provide an efficient method for representing flat surfaces with complex shapes.

Main Methods:

  • Input data (point clouds, meshes) are converted to a half-edge triangular mesh.
  • Backend processing includes mesh smoothing, normal estimation, planar segment extraction, and polygon extraction.
  • Utilizes CPU multi-threading and GPU acceleration.

Main Results:

  • Polylidar3D demonstrates high speed and accuracy across various datasets.
  • Successfully applied to aerial LiDAR for rooftop mapping.
  • Effective for autonomous driving LiDAR in road surface detection.
  • Useful for RGBD cameras in indoor floor/wall detection.

Conclusions:

  • Polylidar3D offers a fast and versatile solution for non-convex polygon extraction from 3D data.
  • Enables efficient representation of flat surfaces with cutouts.
  • Suitable for diverse applications in robotics and computer vision.