Tree point cloud three-dimensional reconstruction method based on local structure and direction perception

Abstract

The invention realizes a tree point cloud three-dimensional reconstruction method based on a local structure and direction perception, and belongs to the technical field of space information. Considering the continuous characteristic of the branch direction and the point cloud density in the local structure, the method deduces a connecting relation of branches at a data missing position, an optimization equation and the iteration process of point cloud repair are designed, points in point cloud data are driven to contract and diffuse in the same time along the direction of a skeleton line by employing skeleton point cloud, the optimized point cloud does not produce extra noises, the contracted skeleton is employed to inherit the space association between adjacent iterations instead of depending on the optimized point cloud, and structural information of the original point cloud is fully retained. The radius of each node of the skeleton is calculated by employing a plant growth model, and the skeleton is expanded to a three-dimensional tree model according to the radius of each node. Compared with the conventional tree three-dimensional reconstruction method, point cloud of a deletion region can be more accurately repaired, and a better modeling result is obtained.

Description

1. Technical field [0001] The invention relates to a tree point cloud reconstruction method based on local structure and direction perception, and belongs to the field of spatial information technology. 2. Background technology [0002] In recent years, lidar technology has developed rapidly. Through high-speed laser scanning measurement, it can quickly obtain three-dimensional coordinate data of a large area of ​​the measured object surface with high precision, and has become an important means to quickly obtain fine and large-scale three-dimensional urban scene data. Ground-based lidar is a non-destructive high-resolution three-dimensional measurement method, which can make up for the shortcomings of existing observation methods, realize the automatic acquisition of geometric structure parameters such as vegetation, and provide the possibility to reconstruct the real three-dimensional structure of trees. [0003] Due to the limitation of measurement conditions, the measure...

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Problems solved by technology

[0003] Due to the limitation of measurement conditions, the measurement data often has high noise, and the complexity of the surrounding scene objects also determines that the occlusion during the measurement process is inevitable, so the measured data is usually incomplete, and some point clouds are missing. In addition, trees generally have complex three-dimensional structures, topological relationships, and rich details, which determines that the reconstruction of ground lidar tree point clouds based on single-directional scanning is extremely challenging, and there are many key technical issues that need to be solved

[0004] In terms of point cloud repair and branch structure connection in data-missing areas, it is impossible to determine in advance whether the connection relationship between branches is correct. Traditional tree modeling methods usually make each point in the cloud move toward the two ends of the branches for data restoration. Causes a large distortion in the skeleton geometric topological association of the tree

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