Automatic extraction method of fine-size information of ancient wooden building component

Abstract

The invention discloses an automatic extraction method of fine-size information of an ancient wooden building component. The method comprises the steps of (1) point cloud slicing processing: obtaininga point cloud model of the ancient wooden building component and performing point cloud slicing processing on the point cloud model according to different characteristic requirements of the ancient wooden building component to obtain a slice point cloud model, (2) extracting contour lines: simplifying the obtained slice point cloud model to obtain clear contour features and extracting contour lines that characterize the contour features to obtain a contour line point cloud model, (3) identifying geometric feature points: identifying the geometric feature points for constructing a numerical model of the ancient wooden building component from the contour point cloud model obtained by extracting the contour lines that characterize the contour features, and (4) extracting fine size information: extracting fine size information expressing an ancient wooden building component numerical model according to a geometric relationship between identified geometric feature points. According to themethod, the automatic extraction of the fine size information of the ancient wooden building component based on the point cloud data can be achieved, and the method is fast and accurate.

Description

technical field [0001] The invention relates to the field of ancient wooden building protection, in particular to an automatic extraction method for fine size information of ancient wooden building components. Background technique [0002] Ancient buildings carry the long history and splendid civilization of a country, and are the crystallization of human wisdom and hard work. Among them, the building system with wooden structure as the main body has a history of thousands of years in our country, has extremely high scientific, cultural and artistic value, and has the most Chinese characteristics. At present, due to the influence of wars, natural disasters and human factors, ancient wooden buildings generally suffer from damages such as overall torsional inclination, foundation settlement, and deformation of beams and beams. A large number of studies have shown that numerical simulation has gradually become an important means of evaluating and improving structural safety pe...

AI Technical Summary

Problems solved by technology

[0005] However, the disadvantages of traditional manual surveying and mapping to obtain the size information of ancient wooden buildings include at least: (1) Due to the limitation of measurement and expression methods of traditional surveying and mapping, usually the measurement results are two-dimensional architectural drawings, and it is difficult to accurately record the current status of ancient buildings. Corner measurement is difficult and the accuracy is not enough

(2) Need to invest a lot of manpower and material resources

(3) Slow speed and poor timeliness

(4) The measurement results obtained have large errors, which largely depend on the personal experience and on-the-spot judgment of the surveyor and mapper

(5) The description of the spatial characteristics of complex heterosexual components is inaccurate, and there is no actual basis for observation only

(6) It is unavoidable that human trampling may damage the ancient wooden architectural heritage

[0007] Therefore, there are at least the following disadvantages in the existing method of directly obtaining the size information of ancient wooden buildings from the point cloud data of the 3D laser scanning technology: (1) The point cloud data of the ancient wooden architectural heritage is huge, and the size information required for establishing a numerical model is relatively large. Less, the contradiction between the two will result in extremely low manual selection efficiency and time-consuming operation

(2) The point cloud data is dense, the point distribution is uneven, the data volume is huge, contains redundancy and noise, and cannot directly reflect the real information of the object. The manual selection error is very large, which largely depends on the personal experience of the processor.

Images