Infrared-based 3D four-dimensional data acquisition method and device

Abstract

The invention provides an infrared-based 3D four-dimensional data acquisition method and device; the method comprises the following steps: preprocessing a 2D color image gathered by one or more zooming color cameras, thus obtaining a 2D high definition color image of a target object; preprocessing an infrared image gathered by an infrared depth camera, thus obtaining depth infrared data of the target object; obtaining 3D point cloud information of each composition point of the target object according to the depth infrared data of the target object; registering and fusing the 2D high definitioncolor image with the 3D point cloud information; obtaining 3D four-dimensional data of the target object according to the 2D high definition color image with the 3D point cloud information fusion result data and the calibration information of the zooming color camera and the infrared depth camera. The method reduces characteristic point extraction, reduces the algorithmic complexity, and improves3D reconstruction precision and efficiency.

Description

technical field [0001] The invention relates to the technical field of biological feature recognition, in particular to an infrared-based 3D four-dimensional data acquisition method and device. Background technique [0002] At present, in order to collect more real and reliable image data, the solution adopted is to use binocular color cameras to collect image data, and then rely on software algorithms to process images through binocular matching and triangulation methods. [0003] In the traditional binocular solution, no light source is projected externally, similar to the arrangement of human eyes, the same environment is observed through two calibrated 2D cameras, and then two images are captured to match the feature points according to the image content, and then calculate the depth . Since there is no prior knowledge and only relies on environmental images for feature matching, the passive binocular stereo vision scheme is more suitable for fields with good light and ...

AI Technical Summary

Problems solved by technology

[0005] 1) When the feature points in the image are not obvious or the resolution is low, it is difficult to extract feature points from the image

[0006] 2) When the scene is similar, the coordinate correspondence between the two images is not linear, and the use of nonlinear transformation has great limitations

[0007] 3) To find feature points from the image and further perform feature matching, a large number of complex algorithms are required in the whole process, and the higher the resolution, the higher the precision required, the more complex the calculation, therefore, high computing performance is required. In addition The final calculation result is not stable

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