三維可能性C樣本面分群法及其在三維物件分割的應用

Three-Dimensional Possibilistic C-Template Shell Clustering and its Application in 3D Object Segmentation

楊子頡(Tzu-Chieh Yang)

指導教授 ： 王才沛

國立交通大學/資訊學院/多媒體工程研究所/碩士(2016年)

摘要

本論文研究目的在於在三維空間內以一個模型匹配出空間內相似的物件。此研究步驟包括四個主要的部份:1.使用Kinect感應器對實景進行3D建模 2.對建模資料進行物件分割切出獨立的物件 3.建立一個模型對每個獨立的物件進行匹配並得到最終結果。第一部分使用Kinect建立出一個點雲(point cloud)，第二部分使用 3D Hough Transform找出點雲中的平面切除後，在使用連通物件標記(connected-component)進行物件分割，第三部分則是本論文重點，透過以面為基準的模型的分群法(Template-Based Shell Clustering)，將模型與分割的點雲進行迭代匹配來找出相似的成果。在實驗結果中，可以看到物體除了可以精准的匹配外，有別以往兩群點與點互相搜索的耗時，更能提高其效益。

關鍵字

點雲 ； 三維霍夫變換 ； 連通元件標記法 ； 可能性C-means分群法

並列摘要

The purpose of this thesis is to use a model to match a similar object in three-dimensional space.This research includes four main parts: First, using the Kinect sensor to take the real world; second, splitting the point cloud into separate items; third, creating a model to match each individual item; lastly, getting the final result. The thesis includes descriptions on using Kinect to establish a point cloud, using 3D Hough Transform to find and remove the cloud points of planes, and using connected-component to separate individual objects. The focus of this thesis is on matching with individual item and manually created models through the Template-Based Shell Clustering that is the process of detecting clusters of particular geometrical shapes through clustering algorithms. In experimental results, we can see accurate matching results.

並列關鍵字

point cloud ； 3D Hough transform ； connect-component ； possibilistic c-means clustering

參考文獻

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