Point-cloud registration using adaptive radial basis functions
Point-cloud registration using adaptive radial basis functions
复制标题
使用自适应径向基函数的点云配准
DOI：
10.1080/10255842.2018.1484914
复制
发表时间：
2018
影响因子：
1.6
通讯作者：
Justin W. Fernandez
中科院分区：
文献类型：
--
作者：
Ju Zhang;D. Ackland;Justin W. Fernandez
摘要 非刚性配准是生物工程模型生成工作流程的常见部分。与常见的基于网格的方法相比，径向基函数由于其无网格特性可以提供更灵活的变形场。我们引入了一种带有迭代结放置的 RBF 非刚性配准的实现，以自适应地减少配准误差。该实现在股骨、半骨盆、下颌骨和腰椎的表面网格上进行了验证。平均配准表面误差范围为0.37至0.99毫米，Hausdorff距离为1.84至2.47毫米，DICE系数为0.97至0.99。该实现可在免费开源 GIAS2 库中使用。
Abstract Non-rigid registration is a common part of bioengineering model-generation workflows. Compared to common mesh-based methods, radial basis functions can provide more flexible deformation fields due to their meshless nature. We introduce an implementation of RBF non-rigid registration with iterative knot-placement to adaptively reduce registration error. The implementation is validated on surface meshes of the femur, hemi-pelvis, mandible, and lumbar spine. Mean registration surface errors ranged from 0.37 to 0.99 mm, Hausdorff distance from 1.84 to 2.47 mm, and DICE coefficients from 0.97 to 0.99. The implementation is available for use in the free and open-source GIAS2 library.