Deep Learning-Based Determination of Optimal Triangles Number of Graphic Object`s Polygonal Model
Author
Romanyuk, Oleksandr
Zavalniuk, Yevhen
Романюк, О. Н.
Завальнюк, Є. К.
Date
2024Metadata
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- Наукові роботи каф. ПЗ [1275]
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
Please use this identifier to cite or link to this item:
https://ir.lib.vntu.edu.ua//handle/123456789/42383