Pipeline 3D Modeling Based on High-Definition Rendering Intelligent Calculation

In the processing of panoramic video, projection mapping is a very critical step. The selection of the projection mapping format will affect the performance, transmission mode, and rendering mode of the panoramic video codec. Therefore, this article starts from the projection mapping format, analyze...

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Bibliographic Details
Main Authors: Li, S. (Author), Zhou, Y. (Author)
Format: Article
Language:English
Published: Hindawi Limited 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 03645nam a2200361Ia 4500
001 10.1155-2022-4580363
008 220425s2022 CNT 000 0 und d
020 |a 1024123X (ISSN) 
245 1 0 |a Pipeline 3D Modeling Based on High-Definition Rendering Intelligent Calculation 
260 0 |b Hindawi Limited  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1155/2022/4580363 
520 3 |a In the processing of panoramic video, projection mapping is a very critical step. The selection of the projection mapping format will affect the performance, transmission mode, and rendering mode of the panoramic video codec. Therefore, this article starts from the projection mapping format, analyzes the mapping process of the standard mapping format, and then proposes a method of rendering panoramic video in the projection mapping format. By analyzing the parallel design schemes of swarm intelligence algorithms under different granularities, this paper proposes a parallel swarm intelligence optimization algorithm design method and then designs and implements a parallel artificial bee colony algorithm. With the help of the ArcGIS Engine development platform, this paper defines the interface for data exchange. With the support of Multipatch format data in ArcGIS software, through secondary development, the three-dimensional pipeline automatic modeling module is established, and the pipeline model is automatically generated. The digital construction and visualization of the company play a driving role. Based on the understanding of the characteristics of the pipeline image itself, combined with the analysis of the shortcomings of the existing methods, this paper proposes a new deep learning-based high-definition rendering solution for the pipeline image. In this paper, the pipeline image is preprocessed, and then the processed pipeline image is converted into a style pipeline image through the pipeline image style transfer technology, and the obtained style pipeline image is postprocessed to enhance the effect. The preprocessing of pipeline images mainly includes pipeline image enhancement and pipeline image filtering operations. Its purpose is to change the distribution of pipeline images to improve the quality of pipeline images and make them more suitable for subsequent style conversion. In the part of pipeline image style conversion, this paper proposes a new deep learning-based pipeline image high-definition rendering network, which consists of three subnetworks: pipeline image feature modeling module, feature model alignment module, and pipeline image re-rendering module. This article has conducted sufficient experiments to fully compare the processing results of the method proposed in this article and other existing methods and at the same time shows the high-quality high-definition rendering results. The experimental results verify the excellent performance of the method proposed in this paper. © 2022 Shao Li and Yu Zhou. 
650 0 4 |a 3D modeling 
650 0 4 |a 3D models 
650 0 4 |a 3d-modeling 
650 0 4 |a Critical steps 
650 0 4 |a Deep learning 
650 0 4 |a Feature models 
650 0 4 |a High definition 
650 0 4 |a Image enhancement 
650 0 4 |a Mapping 
650 0 4 |a Model module 
650 0 4 |a Model-based OPC 
650 0 4 |a Panoramic video 
650 0 4 |a Performance 
650 0 4 |a Pipelines 
650 0 4 |a Rendering (computer graphics) 
650 0 4 |a Swarm intelligence 
650 0 4 |a Three dimensional computer graphics 
650 0 4 |a Video projections 
700 1 |a Li, S.  |e author 
700 1 |a Zhou, Y.  |e author 
773 |t Mathematical Problems in Engineering