摘要
通过获取的数据进行三维建模的精度直接影响后续的设计与加工制造。目前数据采集有多种方式,而单一数据源建模已经难以满足需求,为解决这一问题,提出一种多源数据融合方法。首先运用基于启发式搜索策略的特征线提取算法提取固定模型的融合区域,然后采用迭代最近点算法实现两模型的空间位置匹配,最后基于迭代拉普拉斯变形对两模型进行表面形态融合,实现两者之间融合。数据源采用下颌CT数据以及光学三维扫描模型,试验表明该技术路线稳定可靠,融合过程无形态扭曲,最终结果反映物体真实物理结构,得到既有表面高精度又具有内部结构的三维模型。
Accurately building 3D model by obtained data directly affects the subsequent design and manufacturing. Nowadays there are numerous ways of data acquisition, but modelling from single source of data can＇t meet the demand. In order to solve the problem, a modelling method by multi-source data fusion is presented. A feature line extracting method based on heuristic search strategy is used to acquire the fusion region of fixed model. Iterative closest point registration algorithm is adopted to match the spatial position of two models. Using Laplace iterative deformation of the surface morphology, the fusion of two models is effectively achieved. CT data and optical 3D scanning model of lower jaw is adopted as data source. The experiment show that the technical route is stable and reliable and that no distortion in the fusion process. Final results reflect the real physical structure of object and 3D model has both high-precision surface and the internal structure.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2014年第7期191-198,共8页
Journal of Mechanical Engineering
基金
国家科技支撑计划(2012BAI07B04)
国家自然科学基金(51205192
81271181)
国家高技术研究发展计划(863计划
SS2013AA040801-02)
南航科研基地创新基金(NJ20130015)资助项目
关键词
多源数据
融合
迭代最近点算法
拉普拉斯变形
multi-source data
fusion
iterative closest point
Laplace deformation
