Choi A Ram, Sung Mee Young
Department of Computer Science and Engineering, Incheon National University, Incheon, Korea.
PLoS One. 2017 Sep 26;12(9):e0184334. doi: 10.1371/journal.pone.0184334. eCollection 2017.
Haptics applications such as surgery simulations require collision detections that are more precise than others. An efficient collision detection method based on the clustering of bounding spheres was proposed in our prior study. This paper analyzes and compares the applied effects of the five most common subdivision surface methods on some 3D models for haptic collision detection. The five methods are Butterfly, Catmull-Clark, Mid-point, Loop, and LS3 (Least Squares Subdivision Surface). After performing a number of experiments, we have concluded that LS3 method is the most appropriate for haptic simulations. The more we applied surface subdivision, the more the collision detection results became precise. However, it is observed that the performance becomes better until a certain threshold and degrades afterward. In order to reduce the performance degradation, we adopted our prior work, which was the fast and precise collision detection method based on adaptive clustering. As a result, we obtained a notable improvement of the speed of collision detection.
诸如手术模拟等触觉应用需要比其他应用更精确的碰撞检测。我们之前的研究提出了一种基于包围球聚类的高效碰撞检测方法。本文分析并比较了五种最常用的细分曲面方法在一些用于触觉碰撞检测的3D模型上的应用效果。这五种方法分别是Butterfly、Catmull-Clark、Mid-point、Loop和LS3（最小二乘细分曲面）。经过多次实验，我们得出结论，LS3方法最适合触觉模拟。我们对曲面进行细分的程度越高，碰撞检测结果就越精确。然而，可以观察到，性能在达到某个阈值之前会变得更好，之后则会下降。为了减少性能下降，我们采用了我们之前的工作，即基于自适应聚类的快速精确碰撞检测方法。结果，我们在碰撞检测速度上取得了显著提高。
