3D Model Similarity Measurement Based on Geometric Feature Map
几何特征映射下的3维模型相似性匹配研究

This paper proposes a Geometric Feature Map method bused on multi-viewpoint range images. After every range image of 3D model is phase encoded, a histogram about the planar surface normal and size of 3D model can be worked out. By using principal component analysis, we can obtain a series of range images and the corresponding histogram of 3D model based on the best viewpoint range image. Similarity measurement between two models can be obtained by calculating the distance of the corresponding histograms of two models. The experimental result shows that our method is invariant to the translation, the rotation and the scaling of 3D model and is robust to the simplification of 3D model, and suitable for the classification of 3D model.