A 3D shape descriptor based on spherical harmonics through evolutionary optimization

This paper proposes an optimization approach based on a newly designed parameter set self-evolutionary process. This method improves the discriminability of the original shape descriptor of a 3D model based on spherical harmonics while retaining the efficiency and simplicity of the original shape descriptor. This method captures the critical characteristics, such as the distance, area, and normal distributions of a 3D model extracted along the latitude–longitude directions after the 3D model is normalized in the uniform coordinate frame, and obtains the 3D model׳s features using a spherical harmonics transform. In order to determine the spherical harmonic basis function relationship, an additional weight (0,1) of each spherical harmonic coefficient as random variable is used to search for the optimal variables based on genetic optimization. The resulting transformed features for these random variables are then used as modified shape descriptors. Retrieval performance is examined using the public benchmarks: the Princeton Shape Benchmark, CCCC and NTU databases, and experiments have shown that the optimized additional weight for shape descriptors based on spherical harmonics results in a significant improvement in discriminability.