發(fā)布時間：2018-11-06 19:04

【摘要】：隨著三維測量技術(shù)的不斷發(fā)展,通過數(shù)字掃描設(shè)備能夠有效的獲取現(xiàn)實世界中物理物體的高精度表面模型,并成為獲取3D點云數(shù)據(jù)的主要手段。三維點云模型已經(jīng)被廣泛應(yīng)用于模式識別、三維重建、模型分割等領(lǐng)域,而特征提取作為三維點云模型處理的底層技術(shù)成為圖像領(lǐng)域的研究熱點。本文在總結(jié)特征提取技術(shù)國內(nèi)外研究現(xiàn)狀的基礎(chǔ)上,將馬爾科夫隨機場(Markov Random Field,MRF)模型應(yīng)用在該領(lǐng)域,分別從建立典型的MRF模型和提取顯著特征點建立MRF模型兩方面給出研究思路和求解框架。本文研究內(nèi)容主要包括:1.提出了基于馬爾科夫隨機場的散亂點云全局特征提取算法。該算法基于經(jīng)典的MRF模型,通過觀察點云分布的直方圖擬合高斯分布建立模型,根據(jù)貝葉斯估計將先驗問題轉(zhuǎn)化為最大后驗概率的求解,進一步推導(dǎo)為隨機場最小能量的求解,歸約簡化得到目標函數(shù),求解該函數(shù)并提取特征點。該算法針對傳統(tǒng)算法存在大量的人工調(diào)參及閾值設(shè)置的問題,靈活的融合了典型MRF模型的自適應(yīng)性,有效避免了傳統(tǒng)算法的弊端,并提高了算法的自適應(yīng)性和時間效率。2.提出了基于顯著特征點的散亂點云特征提取算法。該算法的核心思想是對典型的MRF模型進行改進,和前面算法的主要區(qū)別是對隨機場模型的建立方法:通過構(gòu)造點云的顯著度函數(shù)計算散亂點的顯著度,結(jié)合點間測地距和顯著度構(gòu)建Reeb圖,提取顯著特征點,根據(jù)點到顯著特征點以及中心點的距離求MRF隨機場的聯(lián)合密度函數(shù)。該算法繼承了自適應(yīng)性優(yōu)點,同時也避免了上面算法存在初始閾值的設(shè)置及點云數(shù)據(jù)高斯擬合的問題,使點云特征提取完全跳脫出傳統(tǒng)曲面曲線擬合和特征參數(shù)設(shè)置的思維禁錮。3.這兩個特征提取算法主要應(yīng)用于秦兵馬俑碎片的虛擬復(fù)原項目中。實驗結(jié)果表明本文算法均能夠有效提取出兵俑的特征,相對于傳統(tǒng)算法具有自適應(yīng)性和高效性,為后續(xù)兵馬俑虛擬復(fù)原工作奠定了基礎(chǔ)。
[Abstract]:With the development of 3D measurement technology, the high precision surface model of physical objects in the real world can be obtained effectively by digital scanning equipment, and it becomes the main means to obtain 3D point cloud data. 3D point cloud model has been widely used in pattern recognition, 3D reconstruction, model segmentation and other fields. Feature extraction as the bottom technology of 3D point cloud model processing has become the focus of image research. On the basis of summarizing the research status of feature extraction technology at home and abroad, this paper applies Markov Random Field (Markov Random Field,MRF) model to this field. The research ideas and solution framework are given from two aspects: establishing typical MRF model and extracting prominent feature points to establish MRF model. The main contents of this paper are as follows: 1. A global feature extraction algorithm for scattered point clouds based on Markov random field is proposed. Based on the classical MRF model, the model is established by fitting Gao Si distribution with histogram of observation point cloud distribution. According to Bayesian estimation, the priori problem is transformed into the solution of the maximum posterior probability, and the solution of the minimum energy of the random field is further deduced. The objective function is obtained by reduction, the function is solved and the feature points are extracted. Aiming at the problems of manual parameter adjustment and threshold setting in traditional algorithms, the algorithm combines the self-adaptability of typical MRF model flexibly, effectively avoids the disadvantages of traditional algorithms, and improves the self-adaptability and time efficiency of the algorithm. 2. An algorithm for feature extraction of scattered point clouds based on salient feature points is proposed. The core idea of this algorithm is to improve the typical MRF model. The main difference from the previous algorithm is the method of establishing random field model: the salience of scattered points is calculated by constructing the salience function of point cloud. The Reeb map was constructed by geodesic distance and saliency between the combined points, and the joint density function of MRF random field was obtained according to the distance from the point to the significant feature point and the distance from the center point to the significant feature point. The algorithm inherits the advantages of self-adaptability and avoids the problem of initial threshold setting and point cloud data Gao Si fitting. The point cloud feature extraction completely jumps out of the traditional curve fitting and feature parameter setting. 3. 3. These two feature extraction algorithms are mainly used in the virtual restoration project of Qin Terracotta Warriors and horses fragments. The experimental results show that the proposed algorithm can effectively extract the features of the terracotta warriors, and is more adaptive and efficient than the traditional algorithms, which lays a foundation for the subsequent virtual restoration of the terracotta warriors.
【學(xué)位授予單位】：西北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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