Data generation method, device, server and storage medium

A data generation and data technology, which is applied in image data processing, simulation devices and instruments of space navigation conditions, etc., can solve problems such as difficulty in obtaining data, difficulty in achieving real data fidelity, and insufficient comprehensiveness and accuracy in training data, and achieve high accuracy. sexual effect

Inactive Publication Date: 2019-06-07
SOUTH UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, there are mainly two methods for obtaining training data, real data collection and virtual data generation. Obtaining training data through real data collection requires a lot of manpower, hardware and energy costs, and it is difficult to obtain data of vehicles in dangerous situations in extreme weather. , such as data of scenarios such as sudden braking, sharp turns, and car crashes, which makes the training data not comprehensive and accurate enough
Although the method of generating training data through virtual data can obtain data of dangerous scenarios, the data scenarios are not realistic and comprehensive enough, and it is difficult to achieve the fidelity of real data, thus affecting the accuracy of training

Method used

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  • Data generation method, device, server and storage medium
  • Data generation method, device, server and storage medium
  • Data generation method, device, server and storage medium

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Embodiment 1

[0027] figure 1 It is a flowchart of a data generation method in Embodiment 1 of the present invention. The data generation method provided in this embodiment is applicable to the situation where the data is generated through the three-dimensional model, the automatic driving model is trained according to the data, and the automatic driving vehicle is controlled by the automatic driving model to perform automatic driving. The method can be executed by the data generation device , the device may be implemented by software and / or hardware, and the device may be integrated in the server. see figure 1 , the method of this implementation specifically includes the following steps:

[0028] S110. Construct a real three-dimensional model according to the collected real scene data.

[0029] specific, figure 2 is a schematic diagram of building a real three-dimensional model in Embodiment 1 of the present invention, such as figure 2 As shown, the real scene data can reflect the c...

Embodiment 2

[0038] Figure 5 It is a flowchart of a data generation method in Embodiment 2 of the present invention. This embodiment is optimized on the basis of the above-mentioned embodiments, and details that are not described in this embodiment can be found in the above-mentioned embodiments. see Figure 5 , the data generation method provided in this embodiment includes:

[0039] S210, performing fusion according to the image data collected in the real scene and the point cloud data, to obtain a real three-dimensional model.

[0040]Specifically, an image acquisition device and a laser radar are provided in the real scene, and the image acquisition device can collect image data in the real scene, and the image data includes the color and texture of the real scene. Lidar can collect point cloud data of objects in real scenes. The number of feature points in the collected image data is small, so the establishment of a 3D model based on the feature points in the image data is not ac...

Embodiment 3

[0057] Image 6 It is a schematic structural diagram of a data generation device provided by Embodiment 3 of the present invention. The device is suitable for generating data through a three-dimensional model, training the automatic driving model according to the data, and controlling the automatic driving vehicle for automatic driving through the automatic driving model. The device can be realized by software and / or hardware, and can be integrated in in the server. see Image 6 , the device specifically includes:

[0058] The first construction module 310 is used to construct a real three-dimensional model according to the collected real scene data;

[0059] The second construction module 320 is configured to construct a virtual three-dimensional model, and fuse the real three-dimensional model with the virtual three-dimensional model to obtain a three-dimensional model of the scene;

[0060] The acquiring module 330 is configured to acquire the data of the three-dimensio...

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Abstract

The embodiment of the invention discloses a data generation method, device, server and storage medium. The method comprises the following steps of: constructing a real three-dimensional model according to acquired real scene data; constructing a virtual three-dimensional model, and fusing the real three-dimensional model and the virtual three-dimensional model to obtain a scene three-dimensional model; and acquiring the data of the scene three-dimensional model as scene data. By acquiring the acquired real scene data and constructing the real three-dimensional model according to the real scenedata, the model data of the real scene can be obtained; by constructing the virtual three-dimensional model, a plurality of scene models can be obtained, so that the three-dimensional model is more diversified; and by fusing the real three-dimensional model and the virtual three-dimensional model, the obtained scene three-dimensional model has characteristics of real color, texture and the like,and the diversified scene three-dimensional model is more authentic, so that the obtained data is close to the real data, and the accuracy is higher.

Description

technical field [0001] Embodiments of the present invention relate to the field of data generation, and in particular, to a data generation method, device, server, and storage medium. Background technique [0002] At present, in the training of self-driving vehicles, a large amount of training data is required, so that the self-driving vehicles can realize the recognition and target positioning of the surrounding traffic environment according to the training data and deep learning. [0003] At present, there are mainly two methods for obtaining training data, real data collection and virtual data generation. Obtaining training data through real data collection requires a lot of manpower, hardware and energy costs, and it is difficult to obtain data of vehicles in dangerous situations in extreme weather. , such as data of scenarios such as sudden braking, sharp turns, and car crashes, which makes the training data not comprehensive and accurate enough. Although the method of...

Claims

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Application Information

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IPC IPC(8): G09B9/04G06T17/00
Inventor 郝祁马睿
Owner SOUTH UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA
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