Automobile model benchmarking method, equipment and storage medium

Abstract

The invention discloses an automobile model benchmarking method, and the method comprises: collecting a plurality of image data of the automobile model containing coordinate markers through AR equipment, wherein the coordinate markers comprise an original point marker and a coordinate axis marker which are disposed in the automobile model; establishing model coordinate data of the automobile model according to the coordinate marker of each piece of image data in the plurality of pieces of image data; and enabling setting coordinate data of the AR equipment to be subjected to benchmarking through the model coordinate data, and obtaining benchmarking coordinate data, wherein the benchmarking coordinate data coincide with the model coordinate data, and the setting coordinate data are coordinate data of the automobile model in the AR equipment and comprise coordinate data of the coordinate marker. According to the automobile model benchmarking method, the equipment and the storage medium, the benchmarking accuracy of the automobile model and the virtual vehicle data can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of automobiles, in particular to a method, equipment and storage medium for benchmarking automobile models. Background technique [0002] With the rapid development of vehicle technology, the application of automobiles has become more and more extensive. From travel to cargo transportation, a large number of automobiles will be used. The use of automobiles greatly shortens the travel time and cargo transportation time, bringing users better experience. [0003] In the prior art, when the car model is benchmarked, multiple feature points are usually selected on the virtual car data, and then the relative distances between the multiple features are measured, and then the corresponding points are selected on the car model according to the measured relative distances. Feature points, measure the selected feature points through tools, and then compare the measured data with the relative distance, but due to the ...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, when the car model is benchmarked, multiple feature points are usually selected on the virtual car data, and then the relative distances between the multiple features are measured, and then the corresponding points are selected on the car model according to the measured relative distances. Feature points, measure the selected feature points through tools, and then compare the measured data with the relative distance, but due to the manual measurement used when measuring the selected feature points through tools, the accuracy of manual measurement is small , leading to the problem of low benchmarking accuracy between car models and virtual vehicle data

Images