Edwards John, Bajaj Chandrajit
Department of Computer Science, University of Texas at Austin.
Comput Aided Des. 2011 Oct 1;43(10):1296-1306. doi: 10.1016/j.cad.2011.06.019.
Motivated by the need for correct and robust 3D models of neuronal processes, we present a method for reconstruction of spatially realistic and topologically correct models from planar cross sections of multiple objects. Previous work in 3D reconstruction from serial contours has focused on reconstructing one object at a time, potentially producing inter-object intersections between slices. We have developed a robust algorithm that removes these intersections using a geometric approach. Our method not only removes intersections but can guarantee a given minimum separation distance between objects. This paper describes the algorithm for geometric adjustment, proves correctness, and presents several results of our high-fidelity modeling.
出于对正确且稳健的神经元过程三维模型的需求,我们提出了一种从多个物体的平面横截面重建空间逼真且拓扑正确模型的方法。先前从连续轮廓进行三维重建的工作主要集中于一次重建一个物体,这可能会在切片之间产生物体间的交叉。我们开发了一种稳健的算法,使用几何方法消除这些交叉。我们的方法不仅能消除交叉,还能保证物体之间给定的最小分隔距离。本文描述了几何调整算法,证明了其正确性,并展示了我们高保真建模的几个结果。