【摘要】：伴隨著“智慧城市”地提出,對(duì)三維建模技術(shù)提出更高的要求是推進(jìn)“智慧城市”發(fā)展的必要前提。雖然激光三維掃描技術(shù)已經(jīng)存在且廣泛應(yīng)用,但是在獲取大范圍的城市區(qū)域數(shù)據(jù)及建立實(shí)景三維場(chǎng)景方面存在很多不便。在無人機(jī)平臺(tái)的快速更新下,傾斜攝影技術(shù)得到迅猛發(fā)展。傾斜攝影技術(shù)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(shí)及信息豐富等特點(diǎn),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(gè)領(lǐng)域的應(yīng)用中,對(duì)“智慧城市”的發(fā)展起著巨大地促進(jìn)作用�；趦A斜攝影測(cè)量系統(tǒng)中得到三維點(diǎn)云數(shù)據(jù)的關(guān)鍵技術(shù)為多視影像匹配技術(shù),三維點(diǎn)云數(shù)據(jù)帶有豐富的表面紋理信息,存在較強(qiáng)的、直觀的可視性,對(duì)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(diǎn)云數(shù)據(jù)量龐大,對(duì)于后期數(shù)據(jù)處理、管理等極為不便,因此如何實(shí)現(xiàn)影像匹配點(diǎn)云數(shù)據(jù)地精簡(jiǎn),以最少的點(diǎn)可以較好地表示三維模型就是論文主要研究?jī)?nèi)容。論文首先介紹了傾斜攝影技術(shù)的基本原理以及獲取影像匹配點(diǎn)云數(shù)據(jù)的基本流程,然后通過Photoscan軟件得到初始點(diǎn)云數(shù)據(jù),對(duì)其進(jìn)行預(yù)處理得到要進(jìn)行精簡(jiǎn)研究的實(shí)驗(yàn)數(shù)據(jù)。主要研究?jī)?nèi)容為:對(duì)于預(yù)處理后得到的單棟建筑點(diǎn)云實(shí)驗(yàn)數(shù)據(jù),選擇了三種經(jīng)典點(diǎn)云精簡(jiǎn)算法,包括隨機(jī)采樣法、曲率采樣法、均勻格網(wǎng)法,分別對(duì)比了三種方法在不同精簡(jiǎn)率下的點(diǎn)云顯示效果,分析了該三種方法存在的缺點(diǎn)與不足,在此基礎(chǔ)上提出了兩種算法組合的改進(jìn)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(gè)點(diǎn)的法向量,求出相鄰點(diǎn)法向量夾角,根據(jù)法向量夾角確定一臨界值對(duì)點(diǎn)云數(shù)據(jù)進(jìn)行精簡(jiǎn),對(duì)點(diǎn)間法向量夾角比該臨界值大的點(diǎn)數(shù)據(jù)保留不動(dòng),對(duì)夾角小于該臨界值的點(diǎn)數(shù)據(jù)采取等距離采樣的方法,通過設(shè)定不同的采樣間距達(dá)到要求的精簡(jiǎn)率。最后對(duì)改進(jìn)后的算法在表面積、體積上進(jìn)行評(píng)價(jià),并進(jìn)行3D偏差分析,證明改進(jìn)后算法的點(diǎn)云精簡(jiǎn)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(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P23

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