一种基于球面调和描述子的3维模型相似性比较算法

A 3D Model Similarity Matching Algorithm Based on Spherical Harmonics Descriptor

提出了一种基于球面调和描述子的3维模型相似性比较算法。首先,对3维模型进行一分为二的递归分解,然后对每次递归分解得到的3维模型顶点集合进行球面映射得到其球面图像,最后计算所有球面图像的球面调和描述子得到3维模型的特征二叉树。通过对3维模型特征二叉树进行相似性比较可以得到3维模型的相似性。实验结果表明,该算法不仅能较好地比较3维模型相似性,而且对坐标系旋转变换、模型噪声、网格简化和细分具有较好的鲁棒性。

A 3D model similarity matching algorithm based on spherical harmonics descriptor is proposed in this paper. We first use recursive bisection to partition 3D models, and then we get spherical images by mapping partitions into concentric spheres, last we calculate spherical harmonics descriptors of spherical images to get feature binary trees of 3D models. We can get the similarity among 3D models by matching the similarity among feature binary trees. The experimental results indicate that this approach is robust against rotation transform, noise, model degeneracy etc. , and achieves perfect performance.