發(fā)布時(shí)間：2018-01-20 03:52

  本文關(guān)鍵詞： 激光點(diǎn)云 數(shù)據(jù)壓縮 改進(jìn)坐標(biāo)增量法 特征保留 曲面建模　出處：《西安科技大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：近年來(lái),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(cè)量學(xué)、計(jì)算機(jī)視覺(jué)、計(jì)算機(jī)圖形學(xué)、考古學(xué)等領(lǐng)域�，F(xiàn)代科技的進(jìn)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(duì)較慢,成為制約三維激光掃描技術(shù)進(jìn)一步發(fā)展的瓶頸,尤其是針對(duì)海量點(diǎn)云數(shù)據(jù)后期的存儲(chǔ)、處理、傳輸和進(jìn)一步的數(shù)據(jù)應(yīng)用。因此,發(fā)展激光點(diǎn)云數(shù)據(jù)處理理論及方法已成為目前學(xué)術(shù)界研究的熱點(diǎn)。本文詳細(xì)介紹了三維激光掃描技術(shù)及點(diǎn)云數(shù)據(jù)獲取與處理的方法,并對(duì)點(diǎn)云數(shù)據(jù)壓縮及曲面建模算法進(jìn)行了深入研究,具體研究工作總結(jié)如下:首先,針對(duì)三維激光掃描設(shè)備采集的點(diǎn)云數(shù)據(jù)密度大、冗余信息多,現(xiàn)有點(diǎn)云數(shù)據(jù)壓縮算法存在不足的問(wèn)題,將坐標(biāo)增量法中一維掃描線逐點(diǎn)壓縮擴(kuò)展到二維掃描線與掃描線間點(diǎn)云數(shù)據(jù)的壓縮,提出了改進(jìn)坐標(biāo)增量的點(diǎn)云數(shù)據(jù)壓縮算法,并通過(guò)實(shí)例,借助Matlab編程,將該算法的壓縮效果與坐標(biāo)增量法、曲率采樣法、區(qū)域重心法和隨機(jī)采樣法等現(xiàn)有算法的壓縮效果比較發(fā)現(xiàn),對(duì)于按行列存儲(chǔ)的平面或曲面點(diǎn)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(jiǎn)度、速度及算法通用性等方面對(duì)現(xiàn)有算法與改進(jìn)算法的壓縮結(jié)果進(jìn)行了對(duì)比分析,驗(yàn)證了改進(jìn)壓縮算法的可行性。并基于Geomagic Studio軟件對(duì)壓縮前后的點(diǎn)云數(shù)據(jù)進(jìn)行建模,從表面積變化比和3D標(biāo)準(zhǔn)偏差等方面對(duì)本文提出的壓縮算法的壓縮效果進(jìn)行了分析,進(jìn)一步驗(yàn)證了該算法的保真效果與可靠性。最后,針對(duì)曲面建模,主要研究了區(qū)域生長(zhǎng)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(duì)重建過(guò)程中的不足之處進(jìn)行了分析。從而為大數(shù)據(jù)時(shí)代下海量點(diǎn)云數(shù)據(jù)的傳輸、存儲(chǔ)、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(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(lèi)號(hào)】：P225.2

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