This paper proposes a framework based on harmonic mean normalized Laplace---Beltrami spectral descriptor. A series of experiments show that the harmonic mean normalization has better performance for non-rigid 3D retrieval, and it is robust to holes, local scaling, noise and sampling. To better distinguish shapes with fine or rough details, weighting method and fusion method are also employed. Weighting method reduces the negative impact of high-frequency information, and fusion method combines multi-level spectral information in both low and high frequencies. Our approach has better performance than other state-of-the-art methods on both retrieval accuracy and time consumption for stretched non-rigid 3D shapes.
['Yusong Liu', 'Zhixun Su', 'Junjie Cao', 'Hui Wang']
Harmonic mean normalized Laplace---Beltrami spectral descriptor