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.

翻译：物理仿真需要在性能与精度之间进行权衡。在碰撞检测中，一个关键权衡是碰撞体几何的粒度。基于基元的碰撞体（如包围盒）效率较高，而使用原始网格更精确，但计算代价通常较大。近似凸分解方法旨在平衡效率与精度。现有方法可生成高质量分解，但需要大量凸部件，且对输入网格的朝向敏感。我们通过VisACD（一种基于可见性、旋转等变、无交集的GPU加速近似凸分解算法）解决了这些缺陷。该方法用更少的凸部件即可产生高质量分解，对形状朝向不敏感，且效率优于现有方法。