We present octree-embedded BSPs, a volumetric mesh data structure suited for performing a sequence of Boolean operations (iterated CSG) efficiently. At its core, our data structure leverages a plane-based geometry representation and integer arithmetics to guarantee unconditionally robust operations. These typically present considerable performance challenges which we overcome by using custom-tailored fixed-precision operations and an efficient algorithm for cutting a convex mesh against a plane. Consequently, BSP Booleans and mesh extraction are formulated in terms of mesh cutting. The octree is used as a global acceleration structure to keep modifications local and bound the BSP complexity. With our optimizations, we can perform up to 2.5 million mesh-plane cuts per second on a single core, which creates roughly 40-50 million output BSP nodes for CSG. We demonstrate our system in two iterated CSG settings: sweep volumes and a milling simulation.

我们介绍了八叉树嵌入式BSP，这是一种体积网格数据结构，适合于有效地执行一系列布尔运算（迭代CSG）。我们的数据结构从根本上利用了基于平面的几何表示和整数算法来保证无条件的稳健操作。这些通常带来相当大的性能挑战，我们可以通过使用定制的固定精度操作和针对平面切割凸网格的有效算法来克服这些挑战。因此，BSP布尔值和网格提取是根据网格切割来制定的。八叉树用作全局加速结构，以使修改保持在本地并限制BSP的复杂性。通过我们的优化，我们可以在单个核心上每秒执行多达250万个网格平面切割，它为CSG创建了大约40-50百万个输出BSP节点。我们在两个重复的CSG设置中演示了我们的系统：扫掠量和铣削仿真。