Plane expansion method and system based on sparse point cloud and electronic equipment

Abstract

The invention discloses a plane expansion method and system based on sparse point cloud and electronic equipment. The plane expansion method based on the sparse point cloud comprises the following steps: selecting a local region of interest on a current frame image according to geometric centers of projection points of all existing space points belonging to a current plane in a previous frame image on the current frame image; performing spatial point extraction on the current frame image according to the local region of interest to obtain a spatial point to be confirmed; deleting and selecting all the existing space points and the to-be-confirmed space points of the current plane to obtain to-be-updated space points belonging to the current plane; and performing plane information recalculation on the current plane based on the to-be-updated spatial point to obtain a new expanded plane.

Description

technical field [0001] The invention relates to the technical field of plane expansion, in particular to a plane expansion method based on a sparse point cloud, a system thereof, and an electronic device. Background technique [0002] In the AR scene, it is often necessary to construct a three-dimensional space model to realize the function of augmented reality, which makes determining an effective space coordinate system in advance an important prerequisite for affecting the effect of subsequent modeling algorithms. However, since the physical space scene is generally more complex, if a coordinate system is arbitrarily determined, it will often increase the complexity of the subsequent modeling steps and affect the user's visual experience. This requires selecting a specific shape area in the physical space to determine the spatial coordinate system. As a common shape feature, the plane has the advantages of simple structure and good adaptability, and is suitable as the dat...

AI Technical Summary

Problems solved by technology

Although this scheme uses spatially sparse feature points, the running time of the algorithm is greatly accelerated, but the real-time nature of the SLAM system requires that the extracted feature points are often controlled to a certain number, and these points are generally difficult to meet after screening. The minimum number of points required for plane expansion will cause the expansion to stop, making it difficult to continue

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