Abstract: A novel method is proposed to construct 3D shape descriptors by extracting rotation invariants of 3D models. In this method, a 3D model is firstly represented as a collection of spherical functions by using Hadamard transforms, and then the rotation invariants of the spherical functions are extracted by spherical harmonic decomposition. The method can avoid information, loss in the process of representing 3D models by the spherical functions. A shape similarity measure is defined on the extracted rotation invariants. A large amount of experiment results verified that this 3D shape descriptor performs better than some others.

Key words: 3D shape descriptors, shape similarity measure, Hadamard transform, spherical harmonics

摘要： 提出一种新的基于三维模型的旋转不变量的形状描述子。在此方法中,使用Hadamard变换的工具先将三维模型表达成一序列球面函数,然后使用球面调和分析提取这些球面函数的旋转不变量。这一做法能够尽量避免将三维模型表达成球面函数过程中的信息丢失。基于这些旋转不变量给出了一种形状相似性度量。给出了大量试验结果,验证了此描述子的性能要优于其他现有的三维形状描述子。

关键词: 三维形状描述子, 形状相似性度量, Hadamard变换, 球面调和变换