在這篇論文中，我們提出一個新的形狀描述元件來提取三維模型特徵，進一步地應用到三維之模型擷取。過去，三維模型特徵僅使用多對點之間的關係，失去了原本模型的幾何結構，而新的形狀描述元件保留了模型的局部幾何資訊。

首先，在一個三維模型的點上，我們根據局部方向資訊建立一對應的立方網格。每個點上創建的網格便可與模型表面相交而得到二元立體網格。接著，我們使用距離轉換(Distance Transform)減低同種模型間的細微變異影響。收集了各種模型的每個距離轉換立方體(Distance Transform Cube)後，即可使用k-means分群法建立一個DTC字典。立基於此字典我們對每個三維模型產生出DTC分布直方圖，並應用在三維模型擷取系統上。

經由實驗結果我們驗證了此新形狀描述元件的功效，當加入更多幾何資訊可創造出更具功效之模型特徵。

In this thesis, we propose a new 3D shape descriptor to extract representative shape features from 3D models for 3D model retrieval. Our approach computes information from the local shape geometrical structures and employs the bag-of-features representation. First of all, we build the descriptor by creating a cube grid according to the local shape information of one vertex and intersecting this cube with the surface of this 3D model. For the intersected cube, we apply the distance transform to reduce the within-class variation. The Distance-Transform-Cubes (DTCs) sets created by the vertices from many 3D models are then collected for building a DTCs’ dictionary by k-means clustering. As the bag-of-feature method, we finally represent a 3D model based on the DTCs’ dictionary by a 1D histogram shape feature called Distance Transform Cube Histogram (DTCH) to perform the retrieval task. In addition, we combine the DTCH with geometrical information, such as distance and angle computed from the 3D model to generate higher-dimensional DTCH-based shape descriptiors. Our experimental results show the superior performance by using the new descriptors for 3D model retrieval.

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