VisACD: Visibility-Based GPU-Accelerated Approximate Convex Decomposition

TL;DR

VisACD is a GPU-accelerated, visibility-based approximate convex decomposition algorithm that produces high-quality, fewer-part decompositions efficiently, overcoming limitations of prior methods regarding shape orientation sensitivity and part count.

Contribution

We introduce VisACD, a novel visibility-based, rotation-equivariant, and intersection-free ACD algorithm that is GPU-accelerated and improves decomposition quality and efficiency.

Findings

Produces fewer convex parts than prior methods.

Is not sensitive to input shape orientation.

Achieves higher efficiency with GPU acceleration.

Abstract

Physics-based simulation involves trade-offs between performance and accuracy. In collision detection, one trade-off is the granularity of collider geometry. Primitive-based colliders such as bounding boxes are efficient, while using the original mesh is more accurate but often computationally expensive. Approximate Convex Decomposition (ACD) methods strive for a balance of efficiency and accuracy. Prior works can produce high-quality decompositions but require large numbers of convex parts and are sensitive to the orientation of the input mesh. We address these weaknesses with VisACD, a visibility-based, rotation-equivariant, and intersection-free ACD algorithm with GPU acceleration. Our approach produces high-quality decompositions with fewer convex parts, is not sensitive to shape orientation, and is more efficient than prior work.