基于谱对称的形状配准方法

针对现有三维形状配准方法中存在左右翻转的错误匹配问题,提出了基于内蕴对称特征检测的高效形状配准算法。首先,通过热核与几何约束构建模型的内蕴自对称点对;其次,基于谱嵌入特征空间分析提取模型的内蕴对称平面,并依据模型表面法向量有效识别模型的左右结构属性;然后,根据内蕴对称点对获得模型的一致性谱对称结构描述;最后,引入一致性点漂移算法(CPD),实现基于谱对称的非刚性模型的形状配准,有效避免了模型配准中的左右结构翻转问题。实验进一步论证了这种方法不仅有效提高了模型匹配的效率,而且能有效识别同类模型的结构特征,对于非刚性模型的配准具有较强的鲁棒性。

Shape registration method based on spectral symmetry

Aiming at the issue of symmetric flips in the process of 3D shape registration, the paper developed an efficient shape registration algorithm based on intrinsic symmetric feature detection. Firstly, intrinsic symmetric point pairs of the model are constructed by heat kernel signature (HKS) and geometric constraints. Secondly, based on the spectral embedding space analysis, the intrinsic symmetric plane of the model is extracted and the symmetrical properties of the model are effectively identified according to the model surface normal vector, getting intrinsic symmetry point pair. Therefore the consistent spectral symmetry structure of the model is presented. Finally, combining the Coherent Point Drift method, the shape registration of non-rigid model based on spectral symmetry is implemented. The experimental results show that the matching method is efficient and robust to the non-rigid deformable shape matching. Moreover, the structural features in same category models also are effectively identified.