We present a $k$-d tree construction algorithm designed to accelerate rendering of scenes with motion blur, in application scenarios where a $k$-d tree is either required or desired. Our associated data structure focuses on capturing incoherent motion within the nodes of a $k$-d tree and improves both data structure quality and efficiency over previous methods. At building time, we track primitives with motion that is significantly distinct from other primitives within the node, guarantee valid node references and the correctness of the data structure via primitive duplication heuristic and propagation rules. Then, we incorporate filtering heuristics to limit the propagation of primitives, and eliminate some incorrect duplicated references that may invalidate node references. We also demonstrate a strategy for updating existing MBKD for animated scenes. Our experiments with this hierarchy show artifact-free motion-blur rendering using a $k$-d tree, and demonstrate improvements against a traditional BVH with interpolation and a MSBVH structure designed to handle moving primitives, particularly in render time.

我们提出一个 $克$-d 树构造算法旨在加速具有运动模糊的场景的渲染，在以下应用场景中 $克$-d 树是必需的或需要的。我们相关的数据结构专注于捕获节点内的不连贯运动$克$-d 树，并比以前的方法提高了数据结构的质量和效率。在构建时，我们跟踪具有明显不同于节点内其他基元的运动的基元，通过基元重复启发式和传播规则保证有效的节点引用和数据结构的正确性。然后，我们结合过滤启发式来限制基元的传播，并消除一些可能使节点引用无效的不正确的重复引用。我们还展示了一种更新动画场景现有 MBKD 的策略。我们对这种层次结构的实验显示，使用$克$-d 树，并展示了对具有插值和 MSBVH 结构的传统 BVH 的改进，该结构旨在处理移动图元，尤其是在渲染时间。