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

Sampling framework for accurate curvature estimation in discrete surfaces.

Gady Agam1, Xiaojing Tang

  • 1Department of Computer Science, Illinois Institute of Technology, 10 West 31st Street, Chicago, IL 60616, USA. agam@iit.edu

IEEE Transactions on Visualization and Computer Graphics
|September 8, 2005
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

Manzamine-A: Unraveling the Chemical and Biological Tapestry of a Marine-Derived Drug Lead.

Marine drugs·2026
Same author

Structural Transformation of a BRAF Inhibitor into a Selective PKR Inhibitor.

Journal of medicinal chemistry·2026
Same author

Automated detection of cerebral microbleeds on ex-vivo MRI scans of community-based older adults.

NeuroImage·2026
Same author

The molecular glue CLEO4-88 inhibits the ACAA1 thiolase by induced binding to GID4.

Nature chemical biology·2026
Same author

An improved ICEEMDAN-depth hybrid network model integrating multimodal data for the screening of diabetic peripheral neuropathy.

Scientific reports·2026
Same author

MuscleX-DI: an integrated data analysis package for X-ray scanning diffraction imaging experiments.

Journal of synchrotron radiation·2025
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 introduces a new framework for accurate curvature estimation on discrete surfaces. The method offers improved accuracy and robustness against noise and irregular sampling, outperforming existing techniques.

Area of Science:

  • Computer Graphics
  • Computational Geometry
  • Geometric Modeling

Background:

  • Accurate curvature estimation is crucial for various applications, including shape analysis, object recognition, and mesh processing.
  • Existing methods for discrete surface curvature estimation often struggle with noise, irregular sampling, and limited geometric representation.

Purpose of the Study:

  • To propose a novel framework for precise curvature estimation in discrete surfaces.
  • To enhance the representation of local surface geometry through a flexible sampling approach.
  • To quantitatively evaluate the proposed framework against established techniques.

Main Methods:

  • A new framework based on local directional curve sampling of discrete surfaces.
  • Controlled sampling frequency to adapt to local geometry.

Related Experiment Videos

  • Quantitative evaluation using randomly generated Bezier surface patches with analytically computed curvature.

    • Main Results:

    • The proposed framework demonstrates smaller estimation errors compared to common techniques.
    • The method shows reduced sensitivity to low sampling density, sampling irregularities, and noise.
    • The local model offers a greater number of degrees of freedom for better geometric representation.

    Conclusions:

    • The novel framework provides a more accurate and robust solution for discrete surface curvature estimation.
    • The controlled sampling approach enhances the representation of local surface geometry.
    • This method has significant implications for applications requiring precise surface analysis.