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Research on Reconstruction of Building Facade Based on Vehicle-borne LIDAR Data

Author: YangYang
Tutor: ZhangYongSheng
School: PLA Information Engineering University
Course: Photogrammetry and Remote Sensing
Keywords: Vehicle-Borne Laser Scanning System Filtering Scanning Beam Point Cloud Segmentation Random Sampling Consensus Feature Extraction Building Facade Reconstruction
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 284
Quote: 2
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Abstract


Vehicle-Borne Laser Scanning System provides a bran-new technical instrument for the three dimensional data collection of city modeling and reconstruction of building facade. It acts a more and more important role in three dimensional city modeling with the character of obtaining three dimensional information at first hand fleetly and accurately. This dissertation focuses on the theme of buiding facade reconstruction based on vehicle-borne LIDAR (Light Detection and Ranging) data closely, and puts keystone on the study of vehicle-borne LIDAR data filtering, segmtation, feature extraction and reconstruction of building facade, which gets along in theory and arithmetic to a certain extent. The major works implemented in this dissertation are listed as follow:1.The components and position principle of vehicle-borne LIDAR system are expatiated. Then the existing methods of vehicle-borne LIDAR point cloud filtering, building facade point cloud segmentation, feature extraction and reconstruction of building facade are analyzed,the issues which need to be settled are summarized.2.A vehicle-borne LIDAR data point cloud filtering method is put forward based on analyzing the distributing space characters of vehicle-borne LIDAR data along scanning beam. The contrast analysis are carried through filtering experiments compared with Density of Projected Points filtering method, and the efficiency and veracity of the method in this dissertation is proved.3.Segmentation of building facade point cloud based on Random Sampling Consensus is implemented. The r-radicus density in Computer Graphics is introduced to devise segmentation judgement rule, and the segmentation flow is presented. Segmentation efficiency is improved, which is indicated in the segmentation experiments.4.Automatic recognizing strategy is expatiated, and feature extraction method is discussed. A method of extracting window features by grids is brought forward. And two dimensional plain character and three dimensional detail information of building facade are extracted from point cloud data, and then building facade geometrical model of high detail levels is constructed automatically.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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