本文關(guān)鍵詞： 地理場景 三維點云 空間數(shù)據(jù)組織 海量數(shù)據(jù)空間索引 空間數(shù)據(jù)可視化　出處：《南京師范大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著數(shù)字城市的建設(shè),大規(guī)模三維數(shù)據(jù)采集技術(shù)的迅猛發(fā)展,三維激光掃描,航天/航空影像的密集匹配等產(chǎn)生了海量點云數(shù)據(jù),最直接的體現(xiàn)就是點的密度越來越大,點的數(shù)量越來越多,其中利用機載/車載/地面激光掃描系統(tǒng)所獲取的數(shù)據(jù),可達幾十甚至上百G�，F(xiàn)有的三維點云處理軟件,如FARO公司的Focus3D點云掃描處理軟件SCENE5.0、Leica公司的HDS三維激光掃描儀配套的Cyclone6.0以及Polywork、Geomagic等軟件,各有所長,但是還是側(cè)重于解決處理建模方面的問題。對海量點云支持方面較差,其原因是對點云數(shù)據(jù)組織與調(diào)度非優(yōu)化方式。 針對存在的問題,本文在對地理場景三維點云數(shù)據(jù)獲取研究的基礎(chǔ)之上,著重研究了海量三維點云數(shù)據(jù)組織與空間索引,分析了當(dāng)前常用的三維點云數(shù)據(jù)的空間索引方法,提出改進八叉樹的三維點云數(shù)據(jù)的組織與索引,并利用混合索引的方法,以降低內(nèi)存的消耗以及提高查詢的效率。在此基礎(chǔ)之上,綜合利用內(nèi)存文件映射、可見性判別以及多層次LOD技術(shù),降低點云繪制的數(shù)目,可在普通PC機上達到快速、高效的點云繪制。在理論和方法研究的基礎(chǔ)之上,開發(fā)了海量三維點云可視化原型系統(tǒng),驗證本文提出的算法有效性。主要成果可總結(jié)如下： (1)研究了地理場景中三維點云數(shù)據(jù)的獲取,主要有機載/車載/地面三維激光掃描以及航天航空/地面立體攝影影像匹配技術(shù),對不同的獲取方法進行相應(yīng)的評價,總結(jié)了所獲取的點云文件格式,將其統(tǒng)一轉(zhuǎn)化為本文需要的二進制文件形式。 (2)研究了海量點云數(shù)據(jù)的組織與空間索引方法,分析了常用的三維點云數(shù)據(jù)的索引方法并對其進行總結(jié)評價,提出了改進八叉樹的編碼方案,在此基礎(chǔ)上進一步提出了對葉節(jié)點數(shù)據(jù)采用KD樹進行混合索引,降低了內(nèi)存的消耗并提高了檢索的效率。 (3)在對海量三維點云數(shù)據(jù)組織與空間索引基礎(chǔ)之上,在點云可視化時,提出了綜合運用內(nèi)存文件映射、可見性判斷以及多層次LOD等技術(shù),降低在點云繪制時點云的數(shù)量,采取這些優(yōu)化調(diào)度的方式,可在普通PC機上實現(xiàn)對海量點云的可視化。 (4)為驗證本文提出算法,開發(fā)了海量三維點云可視化原型系統(tǒng),驗證了本文方法的有效性。
[Abstract]:With the construction of digital city and the rapid development of large-scale 3D data acquisition technology, 3D laser scanning, space / aviation image dense matching has produced massive point cloud data. The most direct manifestation is that the density of points is increasing and the number of points is increasing. Among them, the data obtained by airborne / vehicle / ground laser scanning system is more and more. Existing 3D point cloud processing software, such as FARO's Focus3D point cloud scanning software SCENE5.0. The HDS 3D laser scanner of Leica company has its own strong points, such as Cyclone6.0 and Polywork Geomagic. However, it is still focused on solving the problem of modeling. It is poor in support of massive point cloud because of the non-optimization of point cloud data organization and scheduling. Aiming at the existing problems, this paper focuses on the organization and spatial index of massive 3D point cloud data based on the research of 3D point cloud data acquisition in geographic scene. The spatial index method of 3D point cloud data is analyzed, and the organization and index of 3D point cloud data based on octree are improved, and the mixed index method is used. In order to reduce the memory consumption and improve the efficiency of query. On the basis of this, the use of memory file mapping, visibility discrimination and multi-level LOD technology to reduce the number of point cloud rendering. On the basis of theoretical and methodological research, a massive 3D point cloud visualization prototype system is developed. The main results can be summarized as follows: 1) the acquisition of 3D point cloud data in geographic scene is studied, including airborne / vehicle / ground 3D laser scanning and aerospace / ground stereo image matching technology. The different acquisition methods are evaluated and the obtained point cloud file format is summarized and transformed into the binary file form which is needed in this paper. Secondly, the organization and spatial index method of massive point cloud data are studied, and the commonly used indexing methods of 3D point cloud data are analyzed and evaluated, and an improved octree coding scheme is proposed. On this basis, the mixed index of leaf node data using KD tree is put forward, which reduces the memory consumption and improves the efficiency of retrieval. On the basis of organizing massive 3D point cloud data and spatial index, this paper puts forward some techniques such as memory file mapping, visibility judgment and multi-level LOD in point cloud visualization. By reducing the number of point clouds when the point clouds are drawn, and adopting these optimal scheduling methods, the visualization of mass point clouds can be realized on ordinary PC computers. In order to verify the proposed algorithm, a massive 3D point cloud visualization prototype system is developed, which verifies the effectiveness of the proposed method.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P208

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本文編號：1469412