Deep Learning-Based Determination of Optimal Triangles Number of Graphic Object`s Polygonal Model

Abstract

In the article, the neural network-based method of predicting the optimal triangles count of scene object‘s polygonal model is proposed. The necessity of polygonal models simplification for providing the highly productive visualization of three-dimensional scenes is analyzed. The main approaches to polygonal models simplification are discussed. The main geometrical-spatial factors that determine the optimal polygons number of object`s surface are indicated. The existing methods of the direct simplified model generation, iterative model generation of optimal complexity, model generation relative to the parameters of temporal rendering equation, prediction of the optimal local density of a particular polygon are described. The advantages and disadvantages of described methods are given. The need in complementing the methods of polygonal model simplification with the prediction of model`s optimal polygons number is justified. The proposed method of optimal polygons number prediction that lies in two-branch neural processing of object`s vector and volume data is described. The development of dataset of 24000 samples that is based on ShapeNet dataset for neural network training is described. The process of optimization of the proposed neural network architecture`s parameters with the usage of the optimization library is characterized. The developed method of optimal triangles number prediction is verified through calculating the accuracy metrics of test dataset approximation. It is shown that the proposed method is more accurate than guessing the triangles number and using feedforward neural networks. The illustrative examples of optimal triangles number prediction for the cars and planes models are provided. In the result, the developed neural network provides faster and more effective