With the urgent demand of consumers for diversified automobile modeling, simple, efficient, and intelligent automobile modeling analysis and modeling method is an urgent problem to be solved in current automobile modeling design. The purpose of this article is to analyze the modeling preference and trend of the current automobile market in time, which can assist the modeling design of new models of automobile main engine factories and strengthen their branding family. Intelligent rapid modeling shortens the current modeling design cycle, so that the product rapid iteration is to occupy an active position in the automotive market. In this article, aiming at the family analysis of automobile front face, the image database of automobile front face modeling analysis was created. The database included two data sets of vehicle signs and no vehicle signs, and the image data of vehicle front face modeling of most models of 22 domestic mainstream brands were collected. Then, this article adopts the image classification processing method in computer vision to conduct car brand classification training on the database. Based on ResNet-8 and other model architectures, it trains and classifies the intelligent vehicle brand classification database with and without vehicle label. Finally, based on the shape coefficient, a 3D wireframe model and a curved surface model are obtained. The experimental results show that the 3D curve model can be obtained based on a single image from any angle, which greatly shortens the modeling period by 92%.

随着消费者对多样化汽车建模的迫切需求，简单，高效，智能的汽车建模分析和建模方法已成为当前汽车建模设计中亟待解决的问题。本文旨在及时分析当前汽车市场的建模偏好和趋势，以协助汽车主机厂新车型的建模设计并加强其品牌家族。智能快速建模缩短了当前的建模设计周期，因此产品快速迭代将在汽车市场上占据主动地位。本文针对汽车前脸的族分析，建立了汽车前脸造型分析的图像数据库。该数据库包含两套车辆标志数据集，没有车辆标志，并收集了22个国内主流品牌大多数车型的车辆前脸造型图像数据。然后，本文采用计算机视觉中的图像分类处理方法对数据库进行汽车品牌分类训练。它基于ResNet-8和其他模型架构，对带有和不带有车辆标签的智能汽车品牌分类数据库进行训练和分类。最后，基于形状系数，获得3D线框模型和曲面模型。实验结果表明，可以从任意角度基于单个图像获得3D曲线模型，从而将建模周期大大缩短了92％。本文采用计算机视觉中的图像分类处理方法对数据库进行汽车品牌分类培训。它基于ResNet-8和其他模型架构，对带有和不带有车辆标签的智能汽车品牌分类数据库进行训练和分类。最后，基于形状系数，获得3D线框模型和曲面模型。实验结果表明，可以从任意角度基于单个图像获得3D曲线模型，从而将建模周期大大缩短了92％。本文采用计算机视觉中的图像分类处理方法对数据库进行汽车品牌分类培训。它基于ResNet-8和其他模型架构，对带有和不带有车辆标签的智能汽车品牌分类数据库进行训练和分类。最后，基于形状系数，获得3D线框模型和曲面模型。实验结果表明，可以从任意角度基于单个图像获得3D曲线模型，从而将建模周期大大缩短了92％。基于形状系数，获得3D线框模型和曲面模型。实验结果表明，可以从任意角度基于单个图像获得3D曲线模型，从而将建模周期大大缩短了92％。基于形状系数，获得3D线框模型和曲面模型。实验结果表明，可以从任意角度基于单个图像获得3D曲线模型，从而将建模周期大大缩短了92％。