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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/71215


    Title: 整合地面光達點雲與近景影像敷貼房屋紋理
    Authors: 詹立菱;Chan,Li-Ling
    Contributors: 土木工程學系
    Keywords: 仿真式房屋模型;地面光達;近景影像;遮蔽修補;重複紋理;Photorealistic Building Model;Terrestrial Laser Scanning (TLS) Data;Close Range Image;Occlusion Removal;Repetitive Texture
    Date: 2016-08-17
    Issue Date: 2016-10-13 12:13:05 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著實境需求增加,二維空間資訊系統逐漸進化為三維。在三維空間資訊的應用中,建物模型屬重要的內容。自動化程度高且真實美觀的仿真式房屋模型建置方法,成為近年來的研究重點之一。本研究使用地面光達掃描與多角度近景影像,搭配已有的房屋模型,整合三種資料,敷貼出無遮蔽且視覺上真實與美觀的擬真房屋模型。
    本研究所提出的方法分為四部分:(1)資料套合,(2)前景物判斷,(3)影像遮蔽區修補,及(4)牆面紋理選擇與敷貼。首先資料套合將整合過後的光達點雲與近景影像資料藉由方位解算統整至工作坐標系統。接著利用光達點雲的深度資訊找出前景物點。利用前景物點反投影影像,偵測出影像遮蔽區並修補。修補過後的立面紋理影像,依照其三維位置的形狀大小分類,而後進入紋理選擇。就重複紋理的目標,利用灰度共生矩陣進行紋理分析,加上影像的亮度資訊,經由篩選與配對後,得出模型中每個立面的紋理影像。
    本研究使用影像的紋理與色調特性分析重複紋理,篩選出最適紋理立面。由實驗結果得知,房屋模型敷貼成果的成功率高,且能呈現視覺上的整齊、有統整性,達成兼具真實與美觀的目標。
    ;As the virtual reality(VR) technology advances, geographic information systems(3D GIS) have been evolved from 2D to 3D. Building model has always been a major part in the applications of 3D GIS. Numerous studies have been working toward the generation of photorealistic building models in automated or semi-automated ways. Façade texture mapping that combines terrestrial laser scanning(TLS) data, close range images is proposed in this research. To strike a balance between reality and visualization, image occlusion detection and texture analysis is used in the process.
    There are four main steps in the proposed method: (1) data registration, (2) forescene elements determination, (3) image occlusion detection and compensation, and (4) façade patch selection and texture mapping. First, a building model, close range images, and TLS data are registered in a unified coordinate system. Then, the forescene occlusion areas are detected by spatial analysis of the TLS point clouds. Forescene elements are backprojected onto images, and we compensate occluded parts from other angle images to create a large number of candidate patches. To recognize similar façade texture, we start from two different aspects, namely, façade patch geometric shape and façade patch texture. Compactness calculation and GLCM analysis are applied on these two characteristics individually. Texture characteristic and intensity value are considered in the analysis of repetitive texture patches to find the optimal façade texture. After the analysis, the optimal façade texture is selected by statistical clustering, accordingly. The experimental results show that the proposed mapping method in this research consider both aesthetic and authentic, and the process is applicable.
    Appears in Collections:[土木工程研究所] 博碩士論文

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