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  "path": "/abs/2604.04244v1",
  "publishedAt": "2026-04-07T00:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Egor Fokin",
    "Manolis Savva"
  ],
  "textContent": "**Authors:** Egor Fokin, Manolis Savva\n\nPhysics-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.",
  "title": "VisACD: Visibility-Based GPU-Accelerated Approximate Convex Decomposition"
}