研究生: |
林佑恩 Lin, You-En |
---|---|
論文名稱: |
考慮大小比例之三維模型的黑白圖形簡化方法 Scale-Aware Black and White Abstraction for 3D Shapes |
指導教授: |
朱宏國
Chu, Hung-Kuo 楊永亮 Yang, Yong-Liang |
口試委員: |
姚智原
Yao, Chih-Yuan 王昱舜 Wang, Yu-Shuen 李潤容 Lee, Ruen-Rone |
學位類別: |
碩士 Master |
系所名稱: |
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論文出版年: | 2017 |
畢業學年度: | 105 |
語文別: | 英文 |
論文頁數: | 39 |
中文關鍵詞: | 黑白單色渲染 、基於圖形的形狀描寫 、考慮大小比例之渲染 、影像切割方法 |
外文關鍵詞: | black-and-white rendering, patch-based shape depiction, scale-aware rendering, image segmentation |
相關次數: | 點閱:4 下載:0 |
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以標示性的記號描寫三維物體形狀的技巧在形狀知識的傳遞中扮演重要的角色。其中,以線條為主的標示方式簡潔明瞭、容易創作,因此受到許多應用的偏好,例如建築設計、醫療視覺化、路標設計等。到目前為止,非照片質感渲染領域中發展出了許多以數位三維模型作為基礎資料,藉由分析其表面特徵來產生單色二維線條並描寫原本物體形狀的技巧。但是,我們注意到以線條為主的描寫方法並非描寫三維物件唯一的方法。現代生活中,許多電腦圖標的設計也會採用黑白的實心圖形來描寫一個物體。以此觀察為基礎,我們設計了一種以黑白實心圖形描寫三維模型的演算法。我們的方法以三維模型作為輸入,並在使用者指定的視角下分析模型在二維投影中產生的圖形與特徵線條。我們以這些圖形與線條為基礎,透過它們在二維投影平面上的連接關係建立一個無向圖。圖中每個頂點包含圖形與線條的幾何資訊。接下來,我們對這個無向圖根據數個觀察法則以及使用者指定的輸出尺寸,透過最佳化方法決定每一個圖形與線條的顏色並渲染成輸出的二維黑白影像。我們的方法能在不同的輸出大小設定下適當地簡化不重要的資訊,透過圖形與線條顏色的變化來強調、保留重要的形狀特徵,呈現出不同於線條為主的三維物件形狀描寫結果。與其他現有的方法比較,我們的結果對於人造物體以及低輸出尺寸下的描寫具有優勢。同時,我們的系統實作也能在即時互動的速度下產生結果。
Illustrative depiction of 3D shapes plays an important role in the communication of shape information. Among different styles, line-based depictions are widely used in areas like architectural CAD, medical visualization and road sign designs because of their simplicity. Many methods have focused on generating line drawings based on 3D shape inputs in the realm of NPR. These methods often operate on the geometrical feature of surfaces to extract feature lines, and use only lines to depict the original shape. However, we noticed that line drawings are not the only way to depict a 3D shape. Black and white, patch-based styles are also widely employed in computer icon designs. On the basis of this observation, we propose a method to generate black and white, patch-based depiction of 3D shapes. Our method takes 3D shapes as input and extracts 2D patches and lines in the projected 2D space of a user-specified viewing point. A non-directed graph is built upon the connectivity of these patches and lines. Geometrical information is encoded in each node of this graph. We then perform a label optimization on this graph with several observed rules and an user-specified output scale. After the optimization, the final image is rendered using the labels obtained. Our method abstracts away less important features while preserving and enhancing important ones with respect to different output scales. The result of our method is superior to other present methods in the context of man-made shapes and small output scales. Our implementation of the method runs in interactive rate and gives the user immediate stylized results.
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