Method and system for regularized three-dimensional modeling of rods based on density peak clustering

Abstract

The invention relates to a rod-shaped object regular three-dimensional modeling method and rod-shaped object regular three-dimensional modeling system based on density peak clustering. The rod-shaped object regular three-dimensional modeling method comprises the steps of performing voxel resampling on original point cloud data which are acquired by an on-vehicle laser radar, eliminating outliers, performing mesh segmentation on a scene point cloud, performing ground point elimination and high-rise point elimination on each grid through elevation straight-through filtering, and respectively performing projection on three coordinate planes of a three-dimensional coordinate system; respectively clustering in three projection planes on each grid in a clustering manner based on a density peak and a distance attribute; limiting the spatial range of the point clouds in a same kind by a regular spatial cubic bounding box, extracting the rod-shaped object in the spatial cubic bounding box according to priori knowledge, and obtaining a rod-shaped member characteristic parameter; and performing modeling for restoring the rod-shaped object. The rod-shaped object regular three-dimensional modeling method and the rod-shaped object regular three-dimensional modeling system can perform quick, efficient and accurate extraction on rod-shaped objects in a majority of environments and integrate with a digital city, thereby realizing fine and true modeling on the rod-shaped objects in the digital city.

Description

technical field [0001] The present invention mainly relates to the field of three-dimensional modeling of digital cities, and in particular to a method and system for extracting rod-shaped objects by means of density peak clustering and modeling them in 3ds Max based on point cloud data obtained by vehicle-mounted laser radar. Background technique [0002] The 3D laser point cloud is the high-precision 3D coordinates of the surface of the object. The coordinates and feature information of the ground object can be directly obtained through the point cloud. It has the characteristics of high density, high precision, and large amount of data. Therefore, the 3D point cloud is an emerging 3D technology. , with its ability to quickly and efficiently restore real scenes, has been welcomed by many researchers in related fields. In a large number of existing research results, the extraction of ground objects for point clouds mainly includes the extraction of buildings and roads. Most...

AI Technical Summary

Problems solved by technology

Due to the lack of a universal pole-shaped object extraction algorithm, it is impossible to obtain accurate regularized characteristic parameters such as the position and height of the pole-shaped objects, and it is impossible to truly restore the detailed information of pole-shaped objects such as utility poles and street trees. It is difficult to meet the requirements of digital city construction. The requirements for ensuring the authenticity of ground features in the model, so that it is impossible to distinguish between different types of utility poles and different varieties of street trees, and it is impossible to accurately reconstruct such ground features

It is precisely because of the above reasons that the current model for rod-shaped objects is very single, and the work of manual integration into digital cities is mostly tedious and repetitive and inefficient, which has become a major constraint on the development of digital cities

In addition, there is no technical solution to fill in the missing point cloud in the current research

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