Abstract: The existing algorithms of iterative point cloud simplification often maintain the original model’s information and occupy large memory during the process of iterative simplification. This paper presents a memoryless iterative algorithm which need not record geometric information of the original point cloud model. This algorithm uses the volume and distance optimizations to calculate the new point and sort order of the point-pair contractions. The experimental results show that the novel algorithm can be smaller in average errors while occupying lower memory.

Key words: point cloud simplification, memoryless simplification, iterative method, point-pair contraction

摘要： 针对目前点云迭代简化算法在简化过程中需要保持原始模型信息而占用较大内存的问题，提出了一种无记忆点云迭代简化算法，简化过程中不需要记录原始模型相关几何信息。该算法使用体积优化和距离优化计算点对收缩后的最优点位置并对点对进行排序。实验表明，该算法可以在内存占用较小的情况下得到误差较小的简化模型。

关键词: 点云简化, 无记忆简化, 迭代方法, 点对收缩