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

Abstract

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.