本文關(guān)鍵詞： 激光點云 數(shù)據(jù)壓縮 改進坐標增量法 特征保留 曲面建模　出處：《西安科技大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,基于各種數(shù)據(jù)采集設(shè)備生成逼真的三維數(shù)字化模型已成為獲取物體表面信息的主要手段之一,廣泛應(yīng)用于測量學、計算機視覺、計算機圖形學、考古學等領(lǐng)域�，F(xiàn)代科技的進步使三維激光掃描硬件設(shè)備快速的發(fā)展,而相應(yīng)數(shù)據(jù)處理技術(shù)的發(fā)展卻相對較慢,成為制約三維激光掃描技術(shù)進一步發(fā)展的瓶頸,尤其是針對海量點云數(shù)據(jù)后期的存儲、處理、傳輸和進一步的數(shù)據(jù)應(yīng)用。因此,發(fā)展激光點云數(shù)據(jù)處理理論及方法已成為目前學術(shù)界研究的熱點。本文詳細介紹了三維激光掃描技術(shù)及點云數(shù)據(jù)獲取與處理的方法,并對點云數(shù)據(jù)壓縮及曲面建模算法進行了深入研究,具體研究工作總結(jié)如下:首先,針對三維激光掃描設(shè)備采集的點云數(shù)據(jù)密度大、冗余信息多,現(xiàn)有點云數(shù)據(jù)壓縮算法存在不足的問題,將坐標增量法中一維掃描線逐點壓縮擴展到二維掃描線與掃描線間點云數(shù)據(jù)的壓縮,提出了改進坐標增量的點云數(shù)據(jù)壓縮算法,并通過實例,借助Matlab編程,將該算法的壓縮效果與坐標增量法、曲率采樣法、區(qū)域重心法和隨機采樣法等現(xiàn)有算法的壓縮效果比較發(fā)現(xiàn),對于按行列存儲的平面或曲面點云數(shù)據(jù),該算法具有較好的壓縮效果。然后,結(jié)合定性與定量的方法從精度、簡度、速度及算法通用性等方面對現(xiàn)有算法與改進算法的壓縮結(jié)果進行了對比分析,驗證了改進壓縮算法的可行性。并基于Geomagic Studio軟件對壓縮前后的點云數(shù)據(jù)進行建模,從表面積變化比和3D標準偏差等方面對本文提出的壓縮算法的壓縮效果進行了分析,進一步驗證了該算法的保真效果與可靠性。最后,針對曲面建模,主要研究了區(qū)域生長法、B樣條曲面擬合算法、反向傳輸神經(jīng)網(wǎng)絡(luò)法等典型的曲面擬合方法,并總結(jié)分析了現(xiàn)有曲面擬合方法中存在的局限性,然后基于Cyclone和Geomagic Studio軟件等重建了大雁塔和盧溝橋上石獅子的三維模型,并對重建過程中的不足之處進行了分析。從而為大數(shù)據(jù)時代下海量點云數(shù)據(jù)的傳輸、存儲、管理與分析顯示提供了一定的參考。
[Abstract]:In recent years, 3D digital models based on various data acquisition devices have become one of the main methods to obtain surface information of objects. They are widely used in surveying, computer vision and computer graphics. Archaeology and other fields. With the development of modern science and technology, 3D laser scanning hardware equipment rapid development, but the development of the corresponding data processing technology is relatively slow, which has become a bottleneck restricting the further development of 3D laser scanning technology. Especially for massive point cloud data storage, processing, transmission and further data applications. Developing the theory and method of laser point cloud data processing has become a hot topic in academic circles. This paper introduces the 3D laser scanning technology and the method of point cloud data acquisition and processing in detail. And the point cloud data compression and surface modeling algorithms are deeply studied. The specific research work is summarized as follows: firstly, the point cloud data collected by 3D laser scanning equipment has a large density and much redundant information. The existing point cloud data compression algorithm is insufficient. The point by point compression of one dimensional scan line in the coordinate increment method is extended to the point cloud data compression between two dimensional scanning lines and scanning lines. A point cloud data compression algorithm with improved coordinate increment is proposed. With the help of Matlab programming, the compression effect of the algorithm is compared with the coordinate increment method and the curvature sampling method. The compression effect of the existing algorithms such as the region barycenter method and the random sampling method is compared. It is found that the algorithm has a better compression effect for the plane or curved surface point cloud data stored by the column and column. Then. Combining qualitative and quantitative methods, the compression results of the existing algorithm and the improved algorithm are compared and analyzed from the aspects of accuracy, simplicity, speed and generality of the algorithm. The feasibility of the improved compression algorithm is verified, and the point cloud data before and after compression are modeled based on Geomagic Studio software. The compression effect of the proposed compression algorithm is analyzed from the surface area variation ratio and 3D standard deviation, and the fidelity effect and reliability of the algorithm are further verified. Finally, the surface modeling is proposed. This paper mainly studies some typical surface fitting methods, such as area growth method, B-spline surface fitting algorithm, reverse transfer neural network method and so on, and summarizes and analyzes the limitations of existing surface fitting methods. Then, based on the software of Cyclone and Geomagic Studio, the 3D model of Lion on the Big Wild Goose Pagoda and Lugou Bridge is reconstructed. The shortcomings of the reconstruction process are analyzed, which provides a certain reference for the transmission, storage, management and display of massive point cloud data in the big data era.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P225.2

【參考文獻】

相關(guān)期刊論文 前10條

1 Yang LU;Kanle SHI;Junhai YONG;Hejin GU;Haichuan SONG;;A B-spline curve extension algorithm[J];Science China(Information Sciences);2016年03期

2 王育堅;譚紹維;荊文鵬;董偉偉;;基于八叉樹空間分割的NURBS曲面重構(gòu)方法[J];計算機工程與設(shè)計;2015年06期

3 方芳;程效軍;;海量散亂點云快速壓縮算法[J];武漢大學學報(信息科學版);2013年11期

4 徐工;程效軍;;基于小波技術(shù)的散亂點云自適應(yīng)壓縮算法[J];同濟大學學報(自然科學版);2013年11期

5 宋大虎;李忠科;孫玉春;;三維散亂點云快速曲面重建算法[J];計算機應(yīng)用研究;2013年08期

6 魏薇;潛偉;;三維激光掃描在文物考古中應(yīng)用述評[J];文物保護與考古科學;2013年01期

7 黃慧敏;王晏民;胡春梅;王國利;;地面激光雷達技術(shù)在故宮保和殿數(shù)字化測繪中的應(yīng)用[J];北京建筑工程學院學報;2012年03期

8 呂冰;鐘若飛;王嘉楠;;車載移動激光掃描測量產(chǎn)品綜述[J];測繪與空間地理信息;2012年06期

9 李超;程浩;王芳;;三維激光掃描儀在林木測量方面的應(yīng)用[J];測繪通報;2011年11期

10 肖春霞;;Multi-Level Partition of Unity Algebraic Point Set Surfaces[J];Journal of Computer Science & Technology;2011年02期

相關(guān)博士學位論文 前2條

1 谷曉英;三維重建中點云數(shù)據(jù)處理關(guān)鍵技術(shù)研究[D];燕山大學;2015年

2 胡國飛;三維數(shù)字表面去噪光順技術(shù)研究[D];浙江大學;2005年

相關(guān)碩士學位論文 前10條

1 劉為宏;點云數(shù)據(jù)曲面重建算法及研究[D];燕山大學;2015年

2 孫鵬飛;基于坐標增量的點云數(shù)據(jù)精簡壓縮分析與實踐[D];西安科技大學;2014年

3 楊璐t，

本文編號：1446736