本文選題：三維重建　切入點(diǎn)：特征點(diǎn)匹配　出處：《山東大學(xué)》2009年博士論文

【摘要】： 三維模型獲取是計(jì)算機(jī)圖形學(xué)和計(jì)算機(jī)視覺(jué)領(lǐng)域的一個(gè)基本研究問(wèn)題。然而,利用建模軟件(比如3D MAX和Maya等)手工進(jìn)行三維模型構(gòu)建是十分繁瑣和代價(jià)昂貴的工作。因此,研究如何從現(xiàn)實(shí)世界直接和快速地獲取三維模型,成為該領(lǐng)域的熱點(diǎn)問(wèn)題。目前,基于現(xiàn)實(shí)物體的三維結(jié)構(gòu)獲取作為一種數(shù)字存儲(chǔ)和記錄技術(shù),在物體建模、場(chǎng)景建模、真實(shí)感繪制、機(jī)器人導(dǎo)航、目標(biāo)識(shí)別和三維測(cè)量等科學(xué)和工程領(lǐng)域以及考古學(xué)、廣告、娛樂(lè)等其他文化領(lǐng)域有廣泛的應(yīng)用需求。 基于現(xiàn)實(shí)物體的三維模型獲取方法主要分為主動(dòng)方法和被動(dòng)方法。其中,主動(dòng)方法以使用三維掃描儀的方法為代表。被動(dòng)方法則指基于二維圖像的三維重建方法。基于圖像的三維建模方法具備低成本,靈活和能夠直接獲取彩色紋理等特點(diǎn),是三維激光掃描等主動(dòng)方法的有益補(bǔ)充。 基于圖像的三維建模方法主要分為基于標(biāo)定圖像和基于未標(biāo)定圖像兩種方法。其中基于標(biāo)定圖像的方法需要在重建場(chǎng)景中預(yù)先放入標(biāo)定物,具有時(shí)間和空間的限制性。基于未定標(biāo)圖像的三維建模方法僅依賴圖像間的特征匹配關(guān)系,克服了基于標(biāo)定圖像方法的限制,具備良好的應(yīng)用前景。目前,基于未定標(biāo)圖像的重建方法往往針對(duì)窄基線圖像序列,這使得重建完整模型需要過(guò)多的圖像數(shù)目,提高了時(shí)間和空間復(fù)雜性。 本文基于多視圖未定標(biāo)圖像的局部特征以及多視圖之間的約束關(guān)系,以構(gòu)建復(fù)雜完整的三維模型為目標(biāo),對(duì)三維重建的整個(gè)流程進(jìn)行了深入研究。主要研究工作和創(chuàng)新點(diǎn)總結(jié)如下: 1.提出了新的圖像特征點(diǎn)描述子 提出了一種新的描述圖像局部特征的方法。該方法首先提取圖像中尺度不變的局部特征點(diǎn),其次對(duì)特征點(diǎn)周圍一定尺寸的鄰域內(nèi)梯度數(shù)據(jù)進(jìn)行歸一化處理,得到特征點(diǎn)像斑,然后采用獨(dú)立成份分析(ICA)技術(shù)提取特征點(diǎn)像斑的獨(dú)立成份,作為特征點(diǎn)的特征描述向量。該種描述子提高了局部特征的獨(dú)特性和匹配精度,可用來(lái)解決寬基線多視圖圖像的特征點(diǎn)匹配問(wèn)題,使得重建完整三維模型需要的圖像數(shù)量較少,有利于降低重建工作的時(shí)間和空間的復(fù)雜性,為三維結(jié)構(gòu)恢復(fù)奠定良好基礎(chǔ)。 2.基于二維信息的三維結(jié)構(gòu)和相機(jī)運(yùn)動(dòng)參數(shù)估計(jì)算法 設(shè)計(jì)并實(shí)現(xiàn)了從二維圖像空間重構(gòu)三維空間的點(diǎn)云和相機(jī)運(yùn)動(dòng)參數(shù)估計(jì)的算法流程。 (1)提出了基于全局優(yōu)化的基礎(chǔ)矩陣求解方法。給出了一種新的使用全局最優(yōu)技術(shù),對(duì)基礎(chǔ)矩陣進(jìn)行非線性估計(jì)的方法。首先,在滿足秩為2的前提下,使用最少變量對(duì)基礎(chǔ)矩陣進(jìn)行參數(shù)化。其次,為基礎(chǔ)矩陣建立非凸的全局最優(yōu)估計(jì)模型,并利用線性矩陣不等式松弛法轉(zhuǎn)化非凸問(wèn)題,使其最終可通過(guò)標(biāo)準(zhǔn)線性矩陣不等式(LMI)工具求解。最后,使用RANSAC迭代框架,基于最優(yōu)圖像距離誤差,對(duì)求解的基礎(chǔ)矩陣進(jìn)行優(yōu)化,進(jìn)一步提高了結(jié)果的魯棒性。 (2)提出了僅依賴基礎(chǔ)矩陣精度的射影空間多視圖遞推公式,并基于此進(jìn)行場(chǎng)景射影重建和度量重建。將射影空間投影矩陣形式化為統(tǒng)一的形式,基于基礎(chǔ)矩陣和增量法,估計(jì)對(duì)應(yīng)不同視圖的投影矩陣。采用雙視圖估計(jì),三視圖局部?jī)?yōu)化,串聯(lián)估計(jì)所有視圖運(yùn)動(dòng)參數(shù)的策略,有效減少估計(jì)過(guò)程的累積誤差。所估計(jì)的射影空間投影矩陣和同時(shí)重構(gòu)的射影空間點(diǎn)云作為自標(biāo)定算法的輸入,標(biāo)定出相機(jī)的內(nèi)參矩陣,從而將投影矩陣和點(diǎn)云從射影空間升級(jí)至度量空間。由于基礎(chǔ)矩陣的估計(jì)具備魯棒性,因此,基于我們的方法所計(jì)算的相機(jī)投影矩陣,穩(wěn)定性高,誤差較小,使重構(gòu)的點(diǎn)云具有良好的精確性。 3.提出了三維點(diǎn)云的優(yōu)化算法。 (1)提出了基于SBA框架和隨機(jī)行走模型的非線性優(yōu)化算法。在對(duì)三維點(diǎn)云進(jìn)行優(yōu)化時(shí),二維匹配點(diǎn)是優(yōu)化算法的輸入,采樣精確的二維匹配點(diǎn)對(duì)提高優(yōu)化算法的性能非常重要。提出一種各向異性的隨機(jī)行走模型,用來(lái)重新采樣圖像空間匹配點(diǎn)。以重采樣的匹配點(diǎn)對(duì),投影矩陣參數(shù)和初步估計(jì)的三維結(jié)構(gòu)為優(yōu)化初值,利用SBA框架進(jìn)行局部和全局優(yōu)化處理。最后在RANSAC框架中進(jìn)行迭代優(yōu)化和最優(yōu)參數(shù)選取。 (2)提出基于圖像輪廓的點(diǎn)云調(diào)整方法。根據(jù)采樣視點(diǎn)圖像空間的輪廓數(shù)據(jù),逆向修整三維空間的點(diǎn)云數(shù)據(jù)。首先,根據(jù)輪廓信息計(jì)算需要調(diào)整的三維點(diǎn)集合M,其次,提出兩種方法,包括步長(zhǎng)調(diào)整法和直接計(jì)算法對(duì)集合M中的點(diǎn)沿其內(nèi)法向進(jìn)行啟發(fā)式調(diào)整。 4.提出了基于馬太效應(yīng)概率模型的多視圖紋理映射算法。 提出了基于多視圖圖像,針對(duì)復(fù)雜三維模型的自動(dòng)紋理映射算法。獲取三維結(jié)構(gòu)的序列圖像,作為紋理圖像,映射至三維模型表面,以增強(qiáng)模型的視覺(jué)效果。在迭代框架中,基于馬太效應(yīng)法則,抽象出模型三角網(wǎng)格所屬最佳紋理圖像的變換概率模型,對(duì)所有輸入的多視圖紋理圖像進(jìn)行自動(dòng)重采樣,并對(duì)網(wǎng)格紋理分布進(jìn)行優(yōu)化,使紋理效果最優(yōu)的同時(shí)使紋理接縫盡量減少。另外,提出了算法進(jìn)行紋理接縫融合和紋理表面空洞修補(bǔ)。
[Abstract]:The 3D reconstruction is a basic research topic in the field of computer graphics and computer vision. However, by using the software (such as 3D MAX and Maya) manual construction of three-dimensional model is very tedious and costly work. Therefore, how to study from the real world directly and quickly obtain the three-dimensional model, has become a hot issue in the field. At present, the three-dimensional structure of the real object to obtain as a digital storage and recording technology based on object modeling, scene modeling, realistic rendering, robot navigation, object recognition and 3D measurement and other fields of science and engineering, archaeology, advertising, entertainment and other cultural fields have broad applications.
The 3D model of real objects acquisition method is divided into active and passive method. The method is based on the method, the active method is represented by three-dimensional scanner. The passive method refers to the method of 3D reconstruction based on 2D images. Image based 3D modeling method which has low cost, flexible and can directly get the color texture, is good of 3D laser scanning active methods.
Image based 3D modeling method based on calibration is mainly divided into two kinds of methods based on image and image. The calibration method based on Uncalibrated Image reconstruction needs in the scene in advance into the calibration object, with limited time and space. The 3D modeling method of uncalibrated image depends only on the feature matching between images based on the relationship, overcome the calibration method based on image, have a good application prospect. At present, the reconstruction method based on uncalibrated images is often for narrow baseline images, which makes the reconstruction of complete model requires the number of image too much, improve the time and space complexity.
In this paper, based on the local characteristics of multi view uncalibrated images and the constraint relationship between multiple views, the whole process of 3D reconstruction is studied in order to build complex and complete 3D models. The main research works and innovations are summarized as follows.
1. a new feature point descriptor is proposed.
A new method is proposed to describe the local image features. The method firstly extracts local feature points in the image scale invariant, then normalized to the neighborhood feature points around the size of the gradient data obtained feature points like spot, and then using independent component analysis (ICA) technique to extract feature points as independent component the spot, as the feature vector description. The descriptor improves the unique local features and matching accuracy, can be used to solve the multi view wide baseline image feature point matching problem, making the reconstruction of complete 3D model images need less, is conducive to reducing the complexity of the reconstruction work of time and space, lay a good foundation for 3D structure recovery.
2. estimation algorithm of 3D structure and camera motion parameters based on two-dimensional information
The algorithm flow of the estimation of the motion parameters of a point cloud and a camera from a two-dimensional image space is designed and realized.
(1) put forward the basis matrix solution method based on global optimization. This paper presents a new global optimal technology, nonlinear method for the estimation of the fundamental matrix. Firstly, to meet the rank 2 under the premise of using the least variable parameters of fundamental matrix. Secondly, based on the global optimal non matrix the convex estimation model, and convert the non convex problem using linear matrix inequality relaxation method, the final by standard linear matrix inequality (LMI) tools to solve. Finally, using RANSAC iteration scheme, the optimal image distance error based on fundamental matrix to solve the optimization, to further improve the robustness of the result.
(2) propose a multi view projective space depends only on the accuracy of the fundamental matrix recursive formula, and based on this scene projective reconstruction and metric reconstruction. The matrix form of projective space projection into a unified form, fundamental matrix and incremental estimation method based on projection matrix corresponding to different views. Estimated by the dual view, three view of local optimization, estimation of motion parameters of all view series strategy, effectively reduce the cumulative error estimation process. The estimated projection matrix and projective space and projective space point cloud reconstruction as a self calibration algorithm for the input, standard reference matrix camera set, which will be the projection matrix and the point cloud from projective space to upgrade to measure space. Due to the estimation of the fundamental matrix robust, therefore, the camera projection matrix, calculated by our method based on high stability, the error is small, the reconstruction of point cloud with good precision It's true.
3. the optimization algorithm of three dimensional point cloud is proposed.
(1) we propose a nonlinear optimization algorithm of SBA framework and random walk model. Based on the three-dimensional point cloud is optimized, the two-dimensional matching point is optimization algorithm for the input, sampling accurate two-dimensional matching points is very important to improve the performance of the proposed algorithm. The random walk model is an anisotropic, re sampling image the space matching point. By matching point resampling of the three-dimensional structure of the projection matrix parameters and preliminary estimates for the optimization of the initial value, the local and global optimization using SBA framework. Finally, iterative optimization and optimal parameter selection in the RANSAC framework.
(2) proposed adjustment method based on the point cloud image contour. According to the contour data sampling view image space, point cloud data in reverse dressing in three-dimensional space. Firstly, according to the contour information to calculate the 3D point need to adjust the set M, secondly, put forward two kinds of methods, including the step adjustment method and direct calculation method of the M collection the point along the inner method to heuristic adjustment.
4. a multi view texture mapping algorithm based on the Matthew effect probability model is proposed.
Propose a multi view image based on texture mapping algorithm for automatic complex 3D model. For image sequences 3D structure, as texture image is mapped to the 3D model surface model, to enhance the visual effect. In the iterative framework, based on the Matthew effect, get the probability model of triangular mesh model transform is the best texture multi view images, the texture image of all input for automatic resampling, and the grid texture distribution is optimized, the optimal texture and texture seams to reduce. In addition, this paper puts forward the method of fusion seam texture and texture surface patch the hole.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2009
【分類號(hào)】：TP391.41

【引證文獻(xiàn)】

相關(guān)期刊論文 前2條

1 趙璐璐;耿國(guó)華;王小鳳;劉倩;;基于未標(biāo)定多幅圖的三維重建算法[J];計(jì)算機(jī)應(yīng)用;2012年10期

2 石仁愛(ài);趙志剛;呂慧顯;趙毅;;基于物體幾何性質(zhì)的單幅圖像三維重建[J];青島大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年01期

相關(guān)博士學(xué)位論文 前1條

1 李靜;基于多視圖的三維景物重建技術(shù)研究[D];廣東工業(yè)大學(xué);2013年

相關(guān)碩士學(xué)位論文 前2條

1 劉俊江;基于多幅圖像的幾何和紋理自動(dòng)重建[D];北京理工大學(xué);2011年

2 邱子鑒;基于改進(jìn)隨機(jī)蕨的增強(qiáng)現(xiàn)實(shí)跟蹤注冊(cè)算法的設(shè)計(jì)與實(shí)現(xiàn)[D];哈爾濱理工大學(xué);2014年

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本文編號(hào)：1700997