Interactive Continuous Collision Detection for Topology Changing Models Using Dynamic Clustering.

Area of Science:

• Computer Graphics
• Computational Geometry
• Scientific Computing

Background:

• Continuous Collision Detection (CCD) is crucial for simulating dynamic physical interactions.
• Existing CCD algorithms for deformable models often involve high computational overhead and precomputation.
• Handling topological changes in deformable models presents a significant challenge for collision detection.

Purpose of the Study:

• To develop a fast, precomputation-free algorithm for continuous collision detection between general triangulated deformable models.
• To address the challenge of topological changes during simulations.
• To improve the efficiency and reduce false positives in collision detection for deformable objects.

Main Methods:

• A fast decomposition algorithm representing mesh boundaries with hierarchical clusters.
• Dynamic bounding volume hierarchy generation through rapid cluster merging.
• Focus on inter-cluster collision checks to minimize computational load.

Main Results:

• The algorithm successfully handles general triangulated models, including those with topological changes.
• Demonstrated 1.4 to 5 times speedup compared to prior CCD algorithms on complex benchmarks.
• Reduced computational overhead and the number of false positives in collision detection.

Conclusions:

• The proposed algorithm offers a significant performance improvement for continuous collision detection of deformable models.
• The method is effective for complex scenarios, including medical simulations and crash analysis.
• This approach provides a more efficient solution for real-time simulation and analysis involving deformable objects.