本文選題：植物建模 + Kinect　； 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：骨架作為三維模型的“緊湊”表示方法,能抽象反映出植物模型的拓?fù)浣Y(jié)構(gòu)以及體態(tài)特征。由于骨架的拓?fù)浣Y(jié)構(gòu)簡單,操作方便,是三維模型表面重建、檢索和匹配的基本要素,因而廣泛應(yīng)用于植物建模、三維動(dòng)畫設(shè)計(jì)、醫(yī)學(xué)影像和測繪學(xué)圖像等領(lǐng)域�，F(xiàn)有的骨架提取算法多以人體和物體為初始模型,基于體素?cái)?shù)據(jù)或網(wǎng)格曲面信息表示三維模型,而直接針對植物點(diǎn)云數(shù)據(jù)進(jìn)行骨架提取方法卻很少。由于植物形態(tài)結(jié)構(gòu)復(fù)雜、自身遮擋比較嚴(yán)重以及常用采集設(shè)備的精度限制,采集到的植物初始云數(shù)據(jù)中存在大量的噪聲,且局部數(shù)據(jù)缺失比較嚴(yán)重,如果采取傳統(tǒng)的骨架提取方法,其正確性和完整性難以保證。因此,本文提出了一種基于Kinect的植物點(diǎn)云數(shù)據(jù)骨架提取的方法,具體內(nèi)容為:(1)以Kd-tree作為數(shù)據(jù)結(jié)構(gòu)進(jìn)行點(diǎn)云的存儲和處理,利用拾取技術(shù)從三維植物模型中交互選取所需點(diǎn)云數(shù)據(jù),并用不同顏色作初始標(biāo)記,對采集出的散亂點(diǎn)云進(jìn)行去噪與配準(zhǔn)處理。(2)通過搜索目標(biāo)點(diǎn)的k近鄰建立點(diǎn)云的拓?fù)潢P(guān)系,采用k均值聚類方法最大限度地保留植物枝干彎曲延伸特征的點(diǎn)云集合,并依據(jù)每個(gè)目標(biāo)點(diǎn)與其相鄰點(diǎn)的夾角關(guān)系生成植物骨架。(3)根據(jù)提取的植物骨架重建三維植物模型,并進(jìn)行真實(shí)感渲染,與傳統(tǒng)網(wǎng)格重建方法比較,本文方法避免了邊緣失真、枝葉遮擋以及掃描漏洞等方面的問題,并較為真實(shí)地還原出植物模型的形態(tài)及細(xì)節(jié)。(4)結(jié)合OpenGL設(shè)計(jì)開發(fā)原型系統(tǒng),實(shí)現(xiàn)骨架提取、表面重建及紋理貼圖等功能,用戶通過交互式界面控制參數(shù),直觀、生動(dòng)地顯示實(shí)驗(yàn)結(jié)果。
[Abstract]:As a "compact" representation of 3D models, skeleton can abstractly reflect the topological structure and body features of plant models. Because of its simple topological structure and convenient operation, skeleton is the basic element of 3D model surface reconstruction, retrieval and matching, so it is widely used in plant modeling, 3D animation design, medical image and surveying and mapping image and so on. Most of the existing skeleton extraction algorithms take human body and object as the initial model and represent the 3D model based on voxel data or mesh surface information. However, there are few methods to extract skeleton directly from plant point cloud data. Because of the complexity of plant morphology and structure, the serious occlusion and the precision limitation of common acquisition equipment, there is a lot of noise in the initial cloud data, and the lack of local data is serious. If the traditional skeleton extraction method is adopted, it is difficult to guarantee its correctness and completeness. Therefore, this paper proposes a method of extracting the skeleton of plant point cloud data based on Kinect. The concrete content is: 1) using Kd-tree as the data structure to store and process the point cloud. The point cloud data is interactively selected from the 3D plant model by pick-up technique, and different colors are used as initial markers to de-noise and register the collected scattered point cloud. The topological relationship of the point cloud is established by searching the k-nearest neighbor of the target point. The k-means clustering method is used to preserve the point cloud set of the plant branch bending and extending characteristics to the maximum extent, and according to the angle relationship between each target point and its adjacent points, the plant skeleton is generated. 3) based on the extracted plant skeleton, the three-dimensional plant model is reconstructed. Compared with the traditional mesh reconstruction method, this method avoids the problems of edge distortion, branch and leaf occlusion and scan loopholes, and so on. The prototype system is designed and developed by combining with OpenGL. The functions of skeleton extraction, surface reconstruction and texture mapping are realized. The user controls the parameters through interactive interface, which is intuitionistic. The results of the experiment are vividly displayed.
【學(xué)位授予單位】：江蘇大學(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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