本文選題：點云 + 預處理�。� 參考：《安徽建筑大學》2017年碩士論文

【摘要】：曲面重構技術在逆向工程、機器視覺、虛擬現(xiàn)實VR(Virtual Reality)、現(xiàn)代醫(yī)療等多個領域中被廣泛使用。隨著三維激光掃描技術的飛速發(fā)展和日益成熟,可通過三維激光掃描技術來獲取古建筑物表面高密度的三維點云數(shù)據(jù)信息,再通過后期的分割優(yōu)化、濾波去噪、法線計算、曲面重構等操作完成對點云數(shù)據(jù)的三維建模。由于支持曲面重構的軟件多存在操作復雜、交互定義過多、對輸入數(shù)據(jù)要求過于嚴苛等問題至今未得到廣泛推廣。然而,對古建筑進行三維建模的通常做法是前期通過人工測量得到建筑物中各個構件的具體物理參數(shù),后期再借助三維建模軟件等方式進行描繪、貼圖等,最終效果不夠真實且誤差較大。通過對徽派建筑中大小構件進行考察,本課題最終選用了徽派建筑所特有的穿斗抬梁式木構架、門樓和欄桿作為研究對象從而展開研究。論文圍繞徽派建筑構件點云的曲面重構系統(tǒng)研究主要探討了以下三方面問題:第一,點云數(shù)據(jù)預處理工作。論文以徽派建筑所特有的穿斗抬梁式木構架為例,對其進行多站點掃描獲得原始點云數(shù)據(jù)。首先,通過改進的迭代最近點算法(簡稱ICP算法,Iterative Closet Point)對多站點云數(shù)據(jù)進行配準拼接,獲得整棟建筑物點云數(shù)據(jù);其次,對配準后點云數(shù)據(jù)使用隨機采樣一致性算法進行分割優(yōu)化,獲得梁柱點云數(shù)據(jù);最后,利用體素網(wǎng)格化法對梁柱點云數(shù)據(jù)中離群點進行采樣,再通過濾波器Statistical OutlierRemoval對離群點進行剔除操作。第二,基于預處理后點云數(shù)據(jù)的曲面重構。通過使用最小二乘法對預處理后點云數(shù)據(jù)進行直接地法線估計推斷,得到梁柱點云數(shù)據(jù)中每一點的法線及其正負向。借助點云庫PCL(Point Cloud Library)開源平臺,分別使用貪婪投影三角化算法、BallPivoting算法以及泊松算法對經(jīng)過預處理且具有法線的梁柱點云數(shù)據(jù)進行曲面重構并同時給出了核心算法步驟,最終得到每種算法相對應的效果圖及參數(shù)信息。第三,曲面重構結果對比。通過對比三種算法在同一構件點云條件下的曲面重構效果圖以及網(wǎng)格面數(shù)、網(wǎng)格頂點數(shù)、孔洞數(shù)、耗時等參數(shù)信息,結果表明泊松算法最終效果圖要明顯優(yōu)于其它兩種方法,尤其是最終結果中沒有孔洞出現(xiàn),這對于曲面重構最終三維模型的完整性輸出至關重要。以上研究借助三維激光掃描技術獲取被測對象的點云原始數(shù)據(jù),通過多邊形網(wǎng)格化等技術實現(xiàn)點云數(shù)據(jù)的分割、濾波、法線估計等預處理操作,最終建立基于泊松算法的徽派建筑構件點云數(shù)據(jù)曲面重構模型。文中所提曲面重構整套研究方案為三維建模技術在建筑中的應用尤其是在古建筑復建、數(shù)字化等領域中提供了新的研究思路。
[Abstract]:Surface reconstruction is widely used in many fields such as reverse engineering, machine vision, virtual reality VR(Virtual reality, modern medicine and so on. With the rapid development and maturity of 3D laser scanning technology, 3D laser scanning technology can be used to obtain high density 3D point cloud data information on the surface of ancient buildings, and then through the later segmentation optimization, filtering and denoising, normal calculation, Surface reconstruction and other operations complete the 3D modeling of point cloud data. Due to the complexity of operation, the definition of interaction and the strict requirement of input data, the software supporting surface reconstruction has not been widely popularized up to now. However, the common method of 3D modeling of ancient buildings is to obtain the physical parameters of each component by manual measurement in the early stage, and to depict them in the later stage by means of 3D modeling software, such as mapping, mapping, and so on. The final effect is not real and the error is large. Through the investigation of the large and small components in the Huizhou architecture, this topic finally selects the unique wooden frame of the bucket lift beam, the gatehouse and the railing as the research object to carry out the research. This paper mainly discusses the following three problems about the surface reconstruction system of Huizhou architectural component point cloud: first, point cloud data preprocessing. Taking Huizhou architecture as an example, the original point cloud data is obtained by multi-site scanning. Firstly, the improved iterative nearest point algorithm (ICP algorithm) is applied to the registration of multi-site cloud data to obtain the whole building point cloud data. After registration point cloud data is segmented and optimized by random sampling consistency algorithm to obtain Liang Zhu point cloud data. Finally, we use voxel mesh method to sample outliers in Liang Zhu point cloud data. Then the outliers are removed by filter Statistical OutlierRemoval. Second, surface reconstruction based on pre-processing point cloud data. By using the least square method to infer directly the normals of pre-processed point cloud data, the normals and their positive and negative directions of each point in Liang Zhu's point cloud data are obtained. With the help of point cloud library PCL(Point Cloud library open source platform, the greedy projection triangulation algorithm BallPivoting algorithm and Poisson algorithm are used to reconstruct the surface of preprocessed and normal Liang Zhu point cloud data, and the core algorithm steps are given. Finally, the corresponding effect diagram and parameter information of each algorithm are obtained. Third, the surface reconstruction results are compared. By comparing the surface reconstruction results of the three algorithms under the same component point cloud condition, as well as the grid surface number, the number of mesh vertices, the number of holes, the time consuming and so on, the results show that the Poisson algorithm is better than the other two methods in the final effect graph. In particular, there are no holes in the final results, which is very important for the integrity output of the final 3D model reconstruction. The above research uses 3D laser scanning technology to obtain the original point cloud data of the measured object, and realizes the preprocessing operations such as point cloud data segmentation, filtering, normality estimation and so on through polygonal gridding technology. Finally, the point cloud data surface reconstruction model based on Poisson algorithm is established. The whole research scheme of surface reconstruction in this paper provides a new research idea for the application of 3D modeling technology in architecture, especially in the field of reconstruction and digitization of ancient buildings.
【學位授予單位】：安徽建筑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU198;P225.2

【參考文獻】

相關期刊論文 前10條

1 胡絲蘭;周明全;稅午陽;武仲科;;一種改進Ball Pivoting的散亂點云數(shù)據(jù)重建算法[J];系統(tǒng)仿真學報;2015年10期

2 曾鋒;楊通;姚山;;點云重構三角網(wǎng)格的生長型神經(jīng)氣算法[J];軟件學報;2013年03期

3 程效軍;何桂珍;;適用于多值曲面修復的空洞邊界提取方法及應用[J];測繪學報;2012年06期

4 于榮歡;鄧寶松;吳玲達;瞿師;;三維標量場并行等值面提取與繪制技術[J];計算機輔助設計與圖形學學報;2012年02期

5 王莫;;三維激光掃描技術在故宮古建筑測繪中的應用研究[J];故宮博物院院刊;2011年06期

6 王建文;杜春梅;;三維激光掃描系統(tǒng)的關鍵性技術研究[J];計算機工程與設計;2010年05期

7 劉金玲;唐棣;;基于泊松方程實現(xiàn)點云的表面重構[J];計算機應用與軟件;2009年04期

8 周克勤;趙煦;丁延輝;;基于激光點云的3維可視化方法[J];測繪科學技術學報;2006年01期

9 李家才;反求工程中一種數(shù)據(jù)平滑的方法[J];機械與電子;2005年11期

10 李必軍,方志祥,任娟;從激光掃描數(shù)據(jù)中進行建筑物特征提取研究[J];武漢大學學報(信息科學版);2003年01期

相關博士學位論文 前1條

1 莫X;基于隱式函數(shù)的曲面重構方法及其應用[D];華中科技大學;2010年

相關碩士學位論文 前9條

1 李志華;基于邊界曲線的細分曲面控制網(wǎng)格的生成方法研究[D];上海交通大學;2015年

2 劉歡;DEM數(shù)據(jù)的預處理與三維重建算法研究[D];北京化工大學;2014年

3 羅誠;基于復雜約束的稀疏點云空間曲面重構方法與實現(xiàn)[D];電子科技大學;2014年

4 張凱;基于泊松方程的三維表面重建算法的研究[D];河北工業(yè)大學;2014年

5 張飛飛;點云數(shù)據(jù)簡化及三維曲面重構[D];吉林大學;2012年

6 周華偉;地面三維激光掃描點云數(shù)據(jù)處理與模型構建[D];昆明理工大學;2011年

7 李競超;基于立體視覺的三維重建[D];北京交通大學;2010年

8 肖華;網(wǎng)格重構及特征提取技術研究[D];浙江大學;2010年

9 戴靜蘭;海量點云預處理算法研究[D];浙江大學;2006年

，

本文編號：1834335