本文選題：近景目標(biāo)　切入點(diǎn)：三維重建　出處：《武漢大學(xué)》2014年博士論文

【摘要】：近景目標(biāo)的三維重建是數(shù)字?jǐn)z影測(cè)量與計(jì)算機(jī)視覺(jué)領(lǐng)域中一項(xiàng)復(fù)雜的綜合性技術(shù),可用于國(guó)民生產(chǎn)各行各業(yè)。本文選取了基于結(jié)構(gòu)光的三維重建技術(shù)、基于輪廓線(xiàn)的三維重建技術(shù)、幾何模型與多視影像配準(zhǔn)三個(gè)典型問(wèn)題中的若干關(guān)鍵技術(shù)進(jìn)行了深入研究,具體研究?jī)?nèi)容和研究結(jié)果如下： (1)單幀投影下的結(jié)構(gòu)光掃描關(guān)鍵技術(shù)。本文完成了偽隨機(jī)投影圖案的設(shè)計(jì)和特性分析,鄰域窗口沿核線(xiàn)方向的全局唯一性分析,提出了一種SEEM快速影像匹配方法,包括核線(xiàn)影像種子點(diǎn)匹配和基于區(qū)域增長(zhǎng)的密集匹配,進(jìn)行了錯(cuò)點(diǎn)剔除和匹配算法并行優(yōu)化等。通過(guò)實(shí)驗(yàn)數(shù)據(jù)提出了兩個(gè)長(zhǎng)度為n的隨機(jī)序列的相關(guān)系數(shù)服從均值為0、標(biāo)準(zhǔn)差為1/(?)n的正態(tài)分布的假設(shè),在此假設(shè)下,使用11×11的窗口進(jìn)行種子點(diǎn)匹配可以在整條核線(xiàn)上保證匹配點(diǎn)的唯一性,使用5×5的窗口進(jìn)行區(qū)域增長(zhǎng)匹配可以在小范圍內(nèi)保證匹配點(diǎn)的唯一性。一旦出現(xiàn)了錯(cuò)誤的匹配,根據(jù)偽隨機(jī)投影圖案的性質(zhì),區(qū)域增長(zhǎng)算法將迅速達(dá)到邊界,這是從匹配點(diǎn)中剔除錯(cuò)點(diǎn)的關(guān)鍵。在掃描距離約為600mm,基線(xiàn)長(zhǎng)度約為276mm時(shí),點(diǎn)云的絕對(duì)精度為0.185mm,相對(duì)精度為3200分之一,像方精度約為0.3像素。在CPU Core2T5850、內(nèi)存2GB的配置下,算法達(dá)到了每秒約40萬(wàn)點(diǎn)的匹配速度。 (2)輪廓線(xiàn)約束下的多視影像三維重建。本文完成了基于圖割的物體輪廓線(xiàn)半自動(dòng)提取,輪廓線(xiàn)的平滑,輪廓線(xiàn)圖像的四叉樹(shù)森林壓縮存儲(chǔ),體素模型和表面網(wǎng)格模型的生成算法,表面網(wǎng)格模型的優(yōu)化。不同的賦權(quán)方式會(huì)得到不同的圖割結(jié)果,但沒(méi)有哪一種賦權(quán)方式具有明顯的優(yōu)勢(shì)。為了盡可能地減少人工交互的工作量,在拍攝影像時(shí)應(yīng)盡量選擇與物體的亮度、顏色相差較大的背景。由于在生成體素模型和表面網(wǎng)格模型時(shí),需要將所有的輪廓線(xiàn)圖像同時(shí)讀入內(nèi)存中(每張影像約10MB),采用四叉樹(shù)森林存儲(chǔ)輪廓線(xiàn)圖像這種近似二值圖像,可以達(dá)到100到300倍的壓縮率,而訪(fǎng)問(wèn)四叉樹(shù)的葉子節(jié)點(diǎn)需要從根節(jié)點(diǎn)開(kāi)始逐層向下搜索,最好情況是1層,最壞的情況是9層。對(duì)于圓形過(guò)渡的曲面,拍攝影像的角度的任意性對(duì)其重建的主觀效果影響不大,適當(dāng)增加影像的數(shù)量就可以得到更精細(xì)的結(jié)果；對(duì)于較大的平面,很容易形成三角形的凸起,只有當(dāng)某一張影像的攝影中心剛好位于或者接近該平面時(shí),才可以消除這種現(xiàn)象,拍攝時(shí)需針對(duì)性地選擇視角；對(duì)于存在凹陷的部分,本章節(jié)的方法從理論和實(shí)踐上都無(wú)法得到好的重建結(jié)果；本章節(jié)中用于實(shí)驗(yàn)的物體表面都缺乏(或者局部缺乏)紋理,無(wú)法使用影像匹配的方法得到更好的重建結(jié)果,是在不使用結(jié)構(gòu)光掃描(或其他相似的技術(shù)手段)前提下,本章節(jié)的方法是最合適的。 (3)幾何模型與多視影像配準(zhǔn)。本文完成了幾何模型與多視影像的粗配準(zhǔn)(空間相似變換),基于互信息的精配準(zhǔn),包括OpenGL渲染圖的生成方法,渲染圖上幾何特征顯著區(qū)域的選擇,灰度聯(lián)合直方圖的統(tǒng)計(jì),基于Powell方法的配準(zhǔn)參數(shù)優(yōu)化等。在粗配準(zhǔn)中,需要首先根據(jù)物體和背景的相對(duì)關(guān)系將影像分成不同的組,每一個(gè)影像組位于同一個(gè)坐標(biāo)系中。影像組坐標(biāo)系與幾何模型坐標(biāo)系之間、不同的影像組坐標(biāo)系系之間存在不同的配準(zhǔn)傳遞策略,在人工選取同名點(diǎn)時(shí),可以根據(jù)實(shí)際情況選擇不同的策略,最終將所有的影像組坐標(biāo)系都配準(zhǔn)到幾何模型坐標(biāo)系中。在精配準(zhǔn)中,OpenGL光照方向的不同、直方圖統(tǒng)計(jì)區(qū)域的選擇都會(huì)影響最終的配準(zhǔn)結(jié)果,光照方向與真實(shí)影像的光照條件越接近,配準(zhǔn)精度越高,只統(tǒng)計(jì)幾何特征的影響大于紋理特征影響的區(qū)域,將得到更好的配準(zhǔn)結(jié)果。對(duì)與具有鏡面反射特性的物體,由于影像的灰度依賴(lài)于物體的幾何曲率變化,故經(jīng)過(guò)基于互信息的優(yōu)化后,配準(zhǔn)參數(shù)的精度得到提高。在一個(gè)影像組僅有1張影像的特殊情況下,舍去縮放系數(shù),僅對(duì)6個(gè)參數(shù)進(jìn)行優(yōu)化,仍可以得到較好的配準(zhǔn)結(jié)果�；诳臻g相似變換的粗配準(zhǔn)需要少量的人工交互,每一個(gè)影像組需要至少3對(duì)同名點(diǎn),每組耗時(shí)在5分鐘之內(nèi)；而基于互信息的精配準(zhǔn)的耗時(shí)也在可接受的范圍之內(nèi)。
[Abstract]:The 3D reconstruction is close range digital photogrammetry and computer vision in the field of a complex technology, can be used in all walks of life of national production. This paper selects the technology of 3D reconstruction based on structured light, 3D reconstruction based on contour lines, some key technologies of the three typical problems of geometric model and multi view image registration in the the in-depth research, specific research contents and results are as follows:
(1) the key technologies of optical scanning structure of single frame projection. This paper has completed the design and analysis of characteristics of pseudo random projection patterns, analysis of the uniqueness of the global neighborhood window along the epipolar direction, SEEM presents a fast image matching method, including dense matching epipolar images based on point matching and seed region growing that was the wrong point detection and matching algorithm and parallel optimization. Through the experimental data presented a correlation coefficient of two with a length of N random sequence with mean 0 and standard deviation of 1/ (?) n normality assumption. Under this assumption, the seed point matching can ensure the uniqueness of the matching points in the entire nuclear line using 11 x 11 window for regional growth, matching can ensure the uniqueness of the matching points in a small range using 5 x 5 window. Once the wrong matching, according to the properties of pseudo random projection pattern, region growth algorithm Will quickly reach the boundary, which is the key to eliminate the wrong matching points. From the point of the scanning distance is about 600mm, the baseline length is about 276mm, the absolute accuracy of point cloud is 0.185mm, the relative accuracy of 3200 quarter, as the accuracy is about 0.3 pixels. In CPU Core2T5850, 2GB memory configuration and the algorithm to achieve the matching speed of about 400 thousand points per second.
(2) contour under the constraints of multi view images of three-dimensional reconstruction. The graph cut object contour extraction based on semi automatic, smooth contour, contour image compression and storage of four forest tree generation algorithm of voxel model and surface mesh model, optimization of the surface mesh model. Different weighting the way to get a different graph cut results, but there is no a weighting approach has obvious advantages. In order to reduce the workload of manual interaction, the image should be chosen as the brightness of the object, the color difference between the larger background. Due to the voxel model and surface mesh model, need to profile line all the image read into memory at the same time (in each image, the image is about 10MB) four tree forest storage contour approximate two value image, can achieve a compression ratio of 100 to 300 times, and access to the four fork tree leaf Festival To start from the root node layer down search, the best is 1, the worst case is the 9 layer. Surface for the circular transition, has little effect on arbitrary image angle on the subjective effect of the reconstruction, increasing the number of images can get more precise results for larger; the plane, it is easy to form the triangle convex, only when a picture of the photography center is located in or near the plane, can eliminate this phenomenon, need to choose from when shooting; for the existence of concave part, method of this chapter from both theory and practice to get good reconstruction results; for the surface experiment are lacking in this chapter (or the lack of local texture), image matching methods cannot be used to get better reconstruction results, is not in use structured light scanning (or other similar technology On the premise of this section, the method in this section is the most appropriate.
(3) geometric model and multi view image registration. The geometric model and multi view image registration (space similarity transformation), the fine registration based on mutual information, including the method of generating OpenGL rendering, geometry rendering salient region selection, combined with gray histogram statistics, Powell method the registration parameters optimization. Based on Rough Registration, need first to divide the image into different groups according to the relation between the object and background, each image group is located in the same coordinate system. Between the image coordinates and the geometric model of group coordinate system, there are different registration transfer strategy between image coordinates the same point in the artificial selection, you can choose a different strategy according to the actual situation, will all the image coordinate system are registered into the geometric model of coordinate system. In the fine registration, OpenGL light with different illumination direction, straight The square of the regional statistical selection will affect the final registration result, the direction of illumination and real image illumination conditions closer, registration accuracy is high, influence only statistical geometrical features than the texture features of the affected area, will get better registration results. And with specular reflection objects due to geometric curvature change of image gray dependent objects, so the optimized based on mutual information, the registration parameters precision has been improved. In a special case of image group only 1 images, to optimize the zoom coefficient, only 6 parameters, you can still get a better registration results. The coarse registration space similarity transformation the need for manual interaction based on a small number of images, each group of at least 3 points, each time in 5 minutes; and based on the time-consuming fine registration of mutual information can be accepted within.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：P234.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李金嶺;劉鸝;喬書(shū)波;郭麗;;關(guān)于三維直角坐標(biāo)七參數(shù)轉(zhuǎn)換模型求解的討論[J];測(cè)繪科學(xué);2010年04期

2 全斌;劉二洋;;金字塔影像結(jié)構(gòu)在影像匹配中的應(yīng)用[J];測(cè)繪通報(bào);2010年03期

3 江剛武;姜挺;王勇;龔輝;;基于單位四元數(shù)的無(wú)初值依賴(lài)空間后方交會(huì)[J];測(cè)繪學(xué)報(bào);2007年02期

4 王晏民;胡春梅;;一種地面激光雷達(dá)點(diǎn)云與紋理影像穩(wěn)健配準(zhǔn)方法[J];測(cè)繪學(xué)報(bào);2012年02期

5 吳曉軍,劉偉軍,王天然;基于八叉樹(shù)的三維網(wǎng)格模型體素化方法[J];工程圖學(xué)學(xué)報(bào);2005年04期

6 陳良鋒;李勇;;偽隨機(jī)序列投影三維測(cè)量中的快速子序列匹配[J];光子學(xué)報(bào);2011年11期

7 唐麗,吳成柯,劉侍剛,顏堯平;基于區(qū)域增長(zhǎng)的立體像對(duì)稠密匹配算法[J];計(jì)算機(jī)學(xué)報(bào);2004年07期

8 朱慶,吳波,趙杰;基于自適應(yīng)三角形約束的可靠影像匹配方法[J];計(jì)算機(jī)學(xué)報(bào);2005年10期

9 陳海龍,李宏;基于MATLAB的偽隨機(jī)序列的產(chǎn)生和分析[J];計(jì)算機(jī)仿真;2005年05期

10 楊恩泉;;3D打印技術(shù)對(duì)航空制造業(yè)發(fā)展的影響[J];航空科學(xué)技術(shù);2013年01期

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