Dense point cloud generation method based on multi-view infrared

Abstract

The invention relates to a dense point cloud generation method based on multi-view infrared. The dense point cloud generation method comprises the following steps: acquiring internal parameters and external parameters of an infrared camera; obtaining a first multi-view low-resolution infrared image and a first high-resolution infrared image by using a multi-view low-resolution infrared camera and a high-resolution infrared camera; constructing a data set; inputting the data set into the dense point cloud generation network to be trained until the loss function converges to obtain a trained dense point cloud generation network; and inputting a multi-view low-resolution infrared image to be generated into the trained dense point cloud generation network to obtain a disparity map of the multi-view low-resolution infrared image and a high-resolution infrared image so as to generate a dense point cloud. According to the method, a multi-view pure vision scheme is adopted, the anti-jamming capability of the scheme is higher, the limitation that laser radars interfere with one another, and active infrared ranging schemes and the like cannot be applied to outdoor and other strong light environments can be avoided, and the robustness of the constructed system is improved.

Description

technical field [0001] The invention belongs to the technical field of infrared image processing, and relates to a method for generating dense point clouds based on multi-view infrared. Background technique [0002] Point cloud data is used to describe the three-dimensional appearance of the scene. By using the data of all points in a three-dimensional coordinate system, the data includes the three-dimensional coordinates X, Y, Z and intensity of each point. There are many ways to collect point cloud data, which can be obtained by lidar, RGB-D camera, binocular vision and other methods. Point cloud data contains a large amount of three-dimensional structural information, so it has a wide range of applications in modeling, mapping, autonomous driving, medical treatment, etc. Point cloud data can be divided into sparse point cloud and dense point cloud according to the density of points in the point cloud data, and the density of points in the dense point cloud is high, so it...

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Problems solved by technology

[0004] However, the disadvantage of this method is that since this method uses lidar as the acquisition device for point cloud generation, on the one hand, the cost of the device is high and the devices are easily interfered with; on the other hand, if other information of the point cloud such as infrared Intensity information needs to be registered before it can be used, and the upper limit of resolution is limited by the highest resolution of the device

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