Calculation Of 3D Shapes Correspondence Based On Random Forest

In the 21 st century,deep learning algorithm promotes the rapid development of artificial intelligence.Among them,machine learning can independently learn the characteristics of data collection and mine the logical structure between data.It has been widely used in many fields,such as natural language processing,computer vision,automatic driving,intelligent robot,and so on.In 3D shape analysis and processing,rapid construction of accurate correspondence between shapes is an important and challenging problem in the fields of computer vision,computer graphics,and related research.For the dense correspondence calculation of rigid matching three-dimensional shapes,the existing methods can establish a good correspondence between two or more shapes.However,for calculating dense correspondence between nonrigid shapes,such as the shape correspondence between partial and complete 3D shapes,the correspondence between complete 3D shape clusters,and the correspondence between 3D shapes with large-scale deformation,the existing algorithms can not get accurate calculation results.Aiming at the problem of dense correspondence calculation between non-rigid threedimensional models,a dense correspondence calculation method based on the random forest is proposed.Firstly,the random forest classifier is used to generate the parametric space of the model descriptor,and the feature descriptor that can correctly describe the maximum difference of the current model is calculated.Then,the source model and the target model are described by using the wave kernel signature descriptor.Finally,the functional maps method is used to calculate the corresponding relationship between models.The experimental results show that the dense correspondence calculation method of a non-rigid three-dimensional model based on random forest can obtain a more accurate initial mapping relationship between models.On this basis,the exact dense correspondence between models can be calculated.For the model with a large number of vertices,the calculation time is short,and high accuracy of correspondence calculation can be obtained in all test data sets.Therefore,it has good applicability.