Fast three-dimensional reconstruction method based on Kinect camera

Abstract

The present invention belongs to the field of three-dimensional reconstruction technology and relates to a fast three-dimensional reconstruction method based on Kinect camera. Compared with the conventional technique, the method of the present invention uses the method of main optical axis constraints to remove the imaging error points in a single-view point cloud and improves the quality of the point cloud to a certain extent, making the three-dimensional reconstruction higher accuracy. In addition to that, considering that the point cloud has poor accuracy and less coincidence, the existing point cloud registration algorithm based on RGB-D data is improved. Although some computational time overhead is increased, the registration accuracy of the algorithm is greatly improved. This means the great possibility of the algorithm converging to a local minimum.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional reconstruction, and relates to a fast three-dimensional reconstruction method based on a kinect camera. Background technique [0002] Three-dimensional reconstruction refers to the establishment of a virtual three-dimensional model of a real object or scene in a computer by certain means, and the virtual three-dimensional model can be analyzed, processed and operated in a computer environment. With the advancement of science and technology, 3D reconstruction has played a huge role in various fields. In the field of virtual reality, object models in virtual scenes can be obtained by 3D reconstruction of real objects. Compared with virtual modeling software such as 3DS Max, objects obtained by 3D reconstruction are more realistic. In 3D printing technology, 3D reconstruction is also a crucial step, and the accuracy of 3D reconstruction directly affects the effect of 3D printing generated ...

AI Technical Summary

Problems solved by technology

However, on the basis of the initial pose matrix, the registration algorithm cannot be optimized for fine registration

Although Henry's RGB-D data registration scheme has tried fine registration, the algorithm is only suitable for situations where the pose difference between adjacent data frames is very small.

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