【摘要】：伴隨著“智慧城市”地提出,對三維建模技術提出更高的要求是推進“智慧城市”發(fā)展的必要前提。雖然激光三維掃描技術已經(jīng)存在且廣泛應用,但是在獲取大范圍的城市區(qū)域數(shù)據(jù)及建立實景三維場景方面存在很多不便。在無人機平臺的快速更新下,傾斜攝影技術得到迅猛發(fā)展。傾斜攝影技術具有獲取數(shù)據(jù)高效、受環(huán)境影響小、成本低、得到模型真實及信息豐富等特點,已經(jīng)被大范圍地投入到土地管理與規(guī)劃、農(nóng)業(yè)、林業(yè)、考古等各個領域的應用中,對“智慧城市”的發(fā)展起著巨大地促進作用�；趦A斜攝影測量系統(tǒng)中得到三維點云數(shù)據(jù)的關鍵技術為多視影像匹配技術,三維點云數(shù)據(jù)帶有豐富的表面紋理信息,存在較強的、直觀的可視性,對于三維模型表面重建效率的提高與成本的降低有很大的幫助。然而影像匹配點云數(shù)據(jù)量龐大,對于后期數(shù)據(jù)處理、管理等極為不便,因此如何實現(xiàn)影像匹配點云數(shù)據(jù)地精簡,以最少的點可以較好地表示三維模型就是論文主要研究內(nèi)容。論文首先介紹了傾斜攝影技術的基本原理以及獲取影像匹配點云數(shù)據(jù)的基本流程,然后通過Photoscan軟件得到初始點云數(shù)據(jù),對其進行預處理得到要進行精簡研究的實驗數(shù)據(jù)。主要研究內(nèi)容為:對于預處理后得到的單棟建筑點云實驗數(shù)據(jù),選擇了三種經(jīng)典點云精簡算法,包括隨機采樣法、曲率采樣法、均勻格網(wǎng)法,分別對比了三種方法在不同精簡率下的點云顯示效果,分析了該三種方法存在的缺點與不足,在此基礎上提出了兩種算法組合的改進方法,其基本思路為:首先構(gòu)建三角格網(wǎng),然后求出三角格網(wǎng)法向量,并根據(jù)三角格網(wǎng)法向量求出每個點的法向量,求出相鄰點法向量夾角,根據(jù)法向量夾角確定一臨界值對點云數(shù)據(jù)進行精簡,對點間法向量夾角比該臨界值大的點數(shù)據(jù)保留不動,對夾角小于該臨界值的點數(shù)據(jù)采取等距離采樣的方法,通過設定不同的采樣間距達到要求的精簡率。最后對改進后的算法在表面積、體積上進行評價,并進行3D偏差分析,證明改進后算法的點云精簡效果要優(yōu)于前三種傳統(tǒng)算法。
[Abstract]:With the proposition of "intelligent city", it is a necessary prerequisite to push forward the development of "intelligent city" by putting forward higher requirements to 3D modeling technology. Although laser 3D scanning technology has been widely used, there are many inconveniences in obtaining large range of urban area data and establishing real scene 3D scene. With the rapid updating of UAV platform, tilt photography technology has been developed rapidly. Tilt photography has been widely used in land management and planning, agriculture, forestry, archaeology and other fields because of its high efficiency in obtaining data, low impact by environment, low cost, real model and abundant information. It plays an important role in promoting the development of "wise city". The key technology to get 3D point cloud data based on tilt photogrammetry system is multi-view image matching technology. 3D point cloud data has abundant surface texture information and has strong visual visibility. It is helpful to improve the efficiency of surface reconstruction and reduce the cost of 3D model. However, because of the huge amount of data of image matching point cloud, it is very inconvenient for the later data processing, so how to realize the simplification of image matching point cloud data, and how to represent the 3D model with the least points is the main research content of this paper. This paper first introduces the basic principle of tilt photography and the basic process of obtaining image matching point cloud data, then obtains the initial point cloud data by Photoscan software, and then preprocesses them to obtain the experimental data to be simplified. The main research contents are as follows: for the experimental data of single building point cloud, three classical point cloud reduction algorithms are selected, including random sampling method, curvature sampling method, uniform grid method. The results of point cloud display under different reduction rates are compared, and the shortcomings and shortcomings of the three methods are analyzed. On the basis of this, two improved algorithms are proposed. The basic ideas are as follows: firstly, the triangular grid is constructed. Then the normal vector of triangular grid is obtained, and the normal vector of each point is obtained according to the normal vector of triangular grid, the angle of the normal vector of adjacent points is obtained, and a critical value is determined according to the angle of the normal vector to simplify the data of point cloud. For the point data whose normal vector angle is larger than the critical value, the point data whose angle is smaller than the critical value is fixed. The method of equal distance sampling is used to obtain the required reduction rate by setting different sampling intervals. Finally, the improved algorithm is evaluated on surface area and volume, and 3D deviation analysis is carried out. It is proved that the improved algorithm is superior to the former three traditional algorithms in point cloud reduction effect.
【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】：P23

【參考文獻】

相關期刊論文 前6條

1 李安福;曾政祥;吳曉明;;淺析國內(nèi)傾斜攝影技術的發(fā)展[J];測繪與空間地理信息;2014年09期

2 李鎮(zhèn)洲;張學之;;基于傾斜攝影測量技術快速建立城市3維模型研究[J];測繪與空間地理信息;2012年04期

3 楊楠;校江超;王明海;;利用點云數(shù)據(jù)的法矢及曲率估算[J];現(xiàn)代制造工程;2010年07期

4 杜曉暉;尹寶才;孔德慧;;一種點云混合簡化算法[J];計算機工程與應用;2007年34期

5 萬軍,鞠魯粵;逆向工程中數(shù)據(jù)點云精簡方法研究[J];上海大學學報(自然科學版);2004年01期

6 黃雪梅,王平江,陳吉紅,張新訪,周濟;三維散亂數(shù)據(jù)三角形網(wǎng)格逼近的一種算法[J];計算機工程與設計;1998年02期

相關碩士學位論文 前5條

1 汪禹芹;機載LiDAR點云數(shù)據(jù)的建筑物提取和模型規(guī)范化研究[D];南京大學;2013年

2 甘桂琴;機載LiDAR點云數(shù)據(jù)濾波方法研究[D];中南大學;2012年

3 倪小軍;點云數(shù)據(jù)精簡及三角網(wǎng)格面快速重構(gòu)技術的研究與實現(xiàn)[D];蘇州大學;2010年

4 杜小燕;點云數(shù)據(jù)的光順去噪與簡化技術的研究與實現(xiàn)[D];蘇州大學;2009年

5 紀松;線陣影像多視匹配自動提取DSM的理論與方法[D];解放軍信息工程大學;2008年

，

本文編號：2251993