Similarity Analysis of 3D Models Based on Convolutional Neural Networks with Threshold

Abstract

The structure of a three-dimensional (3D) model will mutate into various shapes when it does deformation. Indeed, various shapes look different from the original 3D model. What is more, the two-dimensional (2D) image of the deformed 3D model is dissimilar to the 2D image of the undeformed model. Therefore, how to find a proper method to analyze the 3D model similarity always be the research hotspot. In these years, with the wide spread of deep learning technology, the research of similarity and retrieval system of 3D models has set off a new technical revolution. However, the data processing method of the 3D model is distinct with the methods of the 2D image, which is more complex. In consequence, the paper presents the similarity analysis method of 3D models based on convolutional neural networks with threshold (t-CNN). The trained and test datasets are multi-view colored 2D images of 3D models which the color is computed by the heat kernel signature (HKS). Meanwhile, in order to improve the accuracy of retrieval, a threshold is added to the CNN before confirming the final category. The experiments show that the colored dataset construction method proposed in the paper makes the data processing easier and improves the classification precision. Similarity analysis of 3D models based on the t-CNN in recall and precision is better than the other methods.