基于广度搜索的增量式点云表面重建

Incremental Surface Reconstruction of Unorganized Points Based on BFS

将人工智能中广度优先的搜索算法引入散乱点云表面重建领域,借助增量计算思想,基于搜索算法状态不断扩展的特点,渐进均匀地扩展重建整个物体表面.算法以初始三角面片初始化搜索队列,以有向边为搜索元素,借助于八叉树空间划分和搜索约束条件,快速完成最优点评估及三角片重建,具有可视化并行计算、选择性填补空洞以及重建结果与参数弱耦合等特点.实验结果表明,本算法高效、稳定,可以重构任意拓扑结构的二维流形三角形网格.

This paper described an algorithm based on artificial intelligence width-first-search algorithm for surface reconstruction of unorganized points. From the incremental computing idea, it makes full use of the state expanding characteristic of search algorithm. Recurring to octree space division, searching constraint and optimum vertex estimation, the algorithm uses initialized triangle as searching base and orienta- tion edges as searching elements to reconstruct model surface gradually and symmetrically. The proposed algorithm supports parallel computing for visualization and does not depend much on parameters. In addition, holes and gaps can be filled optionally. The experimental results show that this algorithm is effective, robust and works well for models with arbitrary topology.