Binocular visual sense depth information obtaining method based on deep learning

Abstract

The invention discloses a binocular visual sense depth information obtaining method based on deep learning, which belongs to the stereoscopic visual sense technology field and comprises the following steps: 1) obtaining training data; 2) marking the original data set to generate the training data; 3) conducting deep learning network training to the obtained image and depth information; and 4) obtaining the depth map output. According to the invention, the luminance information of the left image and the right image that are mutually correlated is calculated; and through the use of a large amount of image and depth information, training and learning are carried out so as to obtain a disparity data model of the binocular image, and after the actual acquisition of the binocular information and according to the training model, it is possible to rapidly and accurately obtain the stereoscopic information of the current scene so as to successfully obtain the depth. The method transfers calculation amount from depth obtaining to the training process, and the hardware is light in weight without the need of laser and energy. Cost-effective and simple to use, the method can be conveniently expanded. The method does not actively emit energy and is suitable for scenes both indoors and outdoors.

Description

technical field [0001] The invention belongs to the technical field of stereo vision, and in particular relates to a binocular vision depth information acquisition method based on deep learning. Background technique [0002] In recent years, depth information has gained more and more applications in sensors. The technologies for obtaining depth information mainly include binocular stereo matching, TOF (Time of Flight, time of flight), monocular structured light, laser radar and other technologies. These technologies can add additional depth information to the sensor, and have a wide range of applications in image recognition and processing, scene understanding, VR, AR, and robotics. However, the mainstream products currently on the market still have their own scope of use and limitations. For example, Microsoft's Kinect (TOF) can only be used indoors and outdoor scenes with limited light, and the distance is limited; monocular structured light technology needs to emit acti...

AI Technical Summary

Problems solved by technology

However, the mainstream products currently on the market still have their own scope of use and limitations

For example, Microsoft's Kinect (TOF) can only be used indoors and outdoor scenes with limited light, and the distance is limited; monocular structured light technology needs to emit active energy, and it is also not suitable for outdoor scenes with strong light; Binocular stereo matching technology belongs to the field of computer stereo vision. Although this technology is suitable for indoors and outdoors, it is not effective when dealing with missing textures, and there are problems such as precise focusing and calculation time; equipment such as lidar is relatively expensive, and Most of them can only obtain the depth information of the two-dimensional plane

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