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

Fast and reliable collision culling using graphics hardware.

Naga K Govindaraju1, Ming C Lin, Dinesh Manocha

  • 1University of North Carolina at Chapel Hill, NC 27599, USA. naga@cs.unc.edu

IEEE Transactions on Visualization and Computer Graphics
|March 3, 2006
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

Sensitivity to Redirected Walking Considering Gaze, Posture, and Luminance.

IEEE transactions on visualization and computer graphics·2025
Same author

Developing a Machine Learning-Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and Validation Study.

JMIR formative research·2025
Same author

Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action.

Scientific reports·2024
Same author

"May I Speak?": Multi-modal Attention Guidance in Social VR Group Conversations.

IEEE transactions on visualization and computer graphics·2024
Same author

Perceptual Thresholds for Radial Optic Flow Distortion in Near-Eye Stereoscopic Displays.

IEEE transactions on visualization and computer graphics·2024
Same author

An Overview of Enhancing Distance Learning Through Emerging Augmented and Virtual Reality Technologies.

IEEE transactions on visualization and computer graphics·2023
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
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

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

This study introduces a GPU-accelerated culling algorithm for efficient collision detection in complex environments. It enhances accuracy using 2.5D overlap tests, outperforming existing methods for various models.

Area of Science:

  • Computer Graphics
  • Computational Geometry
  • Collision Detection

Background:

  • Collision detection is crucial for complex simulations.
  • Existing object-space culling algorithms face accuracy limitations.
  • GPU acceleration is key for real-time performance.

Purpose of the Study:

  • To develop a reliable and fast culling algorithm for collision detection.
  • To improve accuracy in collision detection despite viewport resolution limits.
  • To enable real-time GPU-based collision queries for diverse models.

Main Methods:

  • Utilizing GPU for fast visibility queries to eliminate non-proximate primitives.
  • Computing Minkowski sums of primitives with spheres for robust overlap tests.
  • Integrating the culling algorithm with CULLIDE for comprehensive collision queries.

Related Experiment Videos

Main Results:

  • Achieved faster and more accurate collision detection compared to prior object-space methods.
  • Successfully addressed accuracy issues from limited viewport resolution.
  • Enabled reliable GPU-based collision queries at interactive rates.

Conclusions:

  • The proposed culling algorithm offers significant improvements in collision detection efficiency and accuracy.
  • The method is effective for various complex models, including non-manifold and deformable geometry.
  • This approach facilitates real-time collision detection in demanding applications.