Research on 3D Rendering Effect of Marine Bionic Packaging Container Based on Deep Learning and Visualization

Jie Ning,Joung Hyung Cho,Xiaonan Ren

doi:10.1155/2022/7067342

Abstract

Three-dimensional rendering includes the test of system development environment and running environment. Three-dimensional rendering influencing factors test: the number and size of parallel blocks affect the average rendering frame rate of 26.9 when the number is 9600 and the size is 10 × 10, and the average rendering frame rate is 13.7 when the number is 600 and the size is 40 × 40. According to relevant data, it is inferred that the average rendering frame rate is higher when the number of parallel blocks is large and the size of parallel blocks is small, and the number is inversely proportional to the size. The rendering window size also has a relative influence on the rendering frame rate. When the window size is 600 × 400, only the terrain frame rate is 77.3, and when the window size is 1200 × 800, the frame rate is 68.1, which shows that when only the terrain frame rate is rendered, the frame rate decreases with the increase in the window size, and its performance is not high when only CPU is used. However, when CPU is combined with GPU, the performance is greatly improved, reaching the highest of 29.3 and the lowest of 21.5, which is much higher than that when only CPU is used. According to the influence of the number of sampling points on the frame rate, the 5 × 5 sampling method and 2 × 2 sampling method proposed in this paper have better frame rate performance than the traditional template shadow method. Through the above data, we know that the number of parallel blocks, size, window size, sampling method, and other factors have a certain impact on the average rendering frame rate. Marine bionics is a marginal subject between marine biology and technical engineering science. Deep networks have had a huge impact on the field of machine learning research and application, but at the same time, they cannot clearly explain the ins and outs of deep networks. People have been working to understand the complex process more thoroughly. Since humans’ cognition and experience of the world mainly come from vision, good visualization can effectively help people understand the deep network and perform effective optimization and adjustment.

Highlights

Academic Editor: Man Fai Leung ree-dimensional rendering includes the test of system development environment and running environment. ree-dimensional rendering influencing factors test: the number and size of parallel blocks affect the average rendering frame rate of 26.9 when the number is 9600 and the size is 10 × 10, and the average rendering frame rate is 13.7 when the number is 600 and the size is 40 × 40
It is inferred that the average rendering frame rate is higher when the number of parallel blocks is large and the size of parallel blocks is small, and the number is inversely proportional to the size. e rendering window size has a relative influence on the rendering frame rate
According to the influence of the number of sampling points on the frame rate, the 5 × 5 sampling method and 2 × 2 sampling method proposed in this paper have better frame rate performance than the traditional template shadow method. rough the above data, we know that the number of parallel blocks, size, window size, sampling method, and other factors have a certain impact on the average rendering frame rate

Summary

Introduction

Academic Editor: Man Fai Leung ree-dimensional rendering includes the test of system development environment and running environment. ree-dimensional rendering influencing factors test: the number and size of parallel blocks affect the average rendering frame rate of 26.9 when the number is 9600 and the size is 10 × 10, and the average rendering frame rate is 13.7 when the number is 600 and the size is 40 × 40. Rough the above data, we know that the number of parallel blocks, size, window size, sampling method, and other factors have a certain impact on the average rendering frame rate. Since humans’ cognition and experience of the world mainly come from vision, good visualization can effectively help people understand the deep network and perform effective optimization and adjustment. By studying the deep learning function mode to improve visualization, 3D rendering principle, and process analysis accuracy, according to the factors affecting 3D rendering to improve its rendering performance, the marine bionic packaging container is manufactured through the principle of marine bionic so as to analyze its 3D rendering effect. Scientific Programming block scanning electron microscope and visual image segmentation algorithm based on deep learning. Literature [15] showed that 3D rendering is especially suitable for free-view video applications. e new depth map compression strategy introduces an effective compression strategy for the shape of the segmented region and predicts the surface shape from the segmented region and a group of samples with regular intervals, so as to effectively compress a few prediction residuals. e marine bionic packaging container is limited by the actual application scene, and the biological movement scene in the marine environment is realized by simulating the 3D technology

Methods

Results

Conclusion