论文标题
通过LR B-Spline表面散射数据近似。关于有效近似的改进策略的研究
Scattered data approximation by LR B-spline surfaces. A study on refinement strategies for efficient approximation
论文作者
论文摘要
局部精制的样条表面（LRB）是一种非常适合散射数据近似的表示。当数据集在某些领域具有局部详细信息并且在其他领域的平滑状态时，LR B-Splines允许自由度的空间分布遵循数据集的变化。近似数据集的LRB表面在精度不符合所需公差的区域进行了完善。在本文中，我们在一项系统的研究中解决了不同的LRB细化策略和表面近似的多项式程度。当将其应用于具有不同结构行为的地理空间数据集时，我们研究了它们对数据量和准确性的影响。精炼策略的性能在某种程度上是连贯的，文章以一些建议结束。总体评估表明，双季度LRB对于测试案例的用途更可取，而我们表示为“完整跨度”的策略具有总体最佳性能。
Locally refined spline surfaces (LRB) is a representation well suited for scattered data approximation. When a data set has local details in some areas and is largely smooth in other, LR B-splines allow the spatial distribution of degrees of freedom to follow the variations of the data set. An LRB surface approximating a data set is refined in areas where the accuracy does not meet a required tolerance. In this paper we address, in a systematic study, different LRB refinement strategies and polynomial degrees for surface approximation. We study their influence on data volume and accuracy when applied to geospatial data sets with different structural behaviour. The performance of the refinement strategies is to some degree coherent and the article concludes with some recommendations. An overall evaluation indicates that bi-quadratic LRB are preferable for the uses cases tested, and that the strategies we denote as 'full span' have the overall best performance.
