本文選題：點(diǎn)云精簡 + 包圍盒法　； 參考：《東華理工大學(xué)》2015年碩士論文

【摘要】：隨著三維激光掃描技術(shù)的發(fā)展,硬件和軟件更新?lián)Q代,使用成本下降,越來越多的領(lǐng)域因此獲得了極大的便利。然而隨著精度要求不斷提高,點(diǎn)云數(shù)據(jù)量大,噪聲點(diǎn)多等問題日益成為三維激光掃描技術(shù)發(fā)展的瓶頸。如何快速準(zhǔn)確地去除噪聲點(diǎn),精簡冗余點(diǎn)云成為研究的熱門領(lǐng)域。論文在總結(jié)三維激光掃描技術(shù)的現(xiàn)狀和采集工作流程的基礎(chǔ)上,以點(diǎn)云數(shù)據(jù)精簡為主要研究對象,分析了原始數(shù)據(jù)到符合用戶要求過程中的重難點(diǎn),本文的研究內(nèi)容和成果如下所示:1.在眾多精簡算法和模型中,選擇了針對點(diǎn)云數(shù)據(jù)集合本身的四種算法:基于包圍盒均勻精簡法,基于包圍盒k鄰域二次曲面擬合法,基于八叉樹k鄰域二次曲面擬合法和基于八叉樹k鄰域法向夾角法。通過定性定量比較,對其異同點(diǎn),精確度、簡化度和效率性等指標(biāo)進(jìn)行分析,直觀的表現(xiàn)出各種精簡算法的適用性和優(yōu)劣性,并提出新問題:(1)對現(xiàn)有方法進(jìn)行改良,對閾值的選取進(jìn)行階梯分化,(2)如何搜索最近點(diǎn)形成k鄰域,或者如何規(guī)避k鄰域的使用。2.根據(jù)對比現(xiàn)有精簡算法,對兩個(gè)問題進(jìn)行了探索,分別提出了解決方法,并從其思路來源,原理,理論模型方面預(yù)測了其可行性,且在具體算例中進(jìn)行了精簡演算。此外,還通過控制變量對其進(jìn)行了與已知方案的橫向?qū)Ρ群妥陨淼目v向?qū)Ρ?得到可行化研究結(jié)論,展望未來發(fā)展趨勢,提出見解。
[Abstract]:With the development of 3D laser scanning technology, the hardware and software are updated and the cost is reduced. More and more fields have been greatly facilitated. However, with the continuous improvement of precision, the problems of large amount of point cloud data and many noise points have become the bottleneck of the development of 3D laser scanning technology. How to quickly and accurately remove noise points and reduce redundant point clouds has become a hot research area. On the basis of summarizing the current situation of 3D laser scanning technology and collecting work flow, this paper takes point cloud data reduction as the main research object, and analyzes the heavy and difficult points in the process from raw data to meeting the user's requirements. The contents and results of this paper are as follows: 1. Among many reduced algorithms and models, four algorithms are selected for point cloud data set: uniform reduction method based on bounding box, Quadric surface fitting method based on k-neighborhood of bounding box, Based on octree k-neighborhood Quadric surface fitting method and octree k-neighborhood normal inclusion method. Through qualitative and quantitative comparison, the similarities and differences, accuracy, simplification and efficiency of the index are analyzed, which directly show the applicability and advantages of various simplified algorithms, and put forward new problems: (1) to improve the existing methods, The threshold is divided into steps. (2) how to search the nearest point to form k neighborhood, or how to avoid the use of k neighborhood. According to the comparison of the existing reduction algorithms, the two problems are explored, and the solutions are put forward respectively, and the feasibility is predicted from the sources, principles and theoretical models of the methods, and the simplified calculation is carried out in a concrete example. In addition, the control variables are compared with the known scheme and their own longitudinal comparison, and the feasible research conclusions are obtained, the future development trend is prospected, and some opinions are put forward.
【學(xué)位授予單位】：東華理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.41;TN249

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 王旭;王昶;;Riegl VZ-400三維激光掃描儀數(shù)據(jù)的建模的研究[J];北京測繪;2013年02期

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本文編號：2069099