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研究生: 張登凱
Chang, Teng-Kai
論文名稱: 具樂高®平滑化之立體模型風格化技術
3D LEGO® Generation Using Studs Not On Top Technique
指導教授: 紀明德
Chi, Ming-Te
口試委員: 紀明德
Chi, Ming-Te
姚智原
Yao, Chih-Yuan
朱宏國
Chu, Hung-Kuo
學位類別: 碩士
Master
系所名稱: 理學院 - 資訊科學系
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 56
中文關鍵詞: 樂高平滑化風格化
外文關鍵詞: LEGO, Stylization, Smooth-lize
DOI URL: http://doi.org/10.6814/NCCU202101407
相關次數: 點閱:48下載:2
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  • 自從樂高®積木發售以來,已經成為廣為人知的積木玩具之一,並且也成為了廣受歡迎的創作媒介。而在電腦科學領域,也有許多自動生成2D與3D結構的研究,但大多數僅使用樂高的基本磚進行建構,外觀上仍保留了體素化的特徵。本研究透過加入不同尺寸的斜面磚塊,改良生成的樂高結構,使結果的外觀更加貼近使用者輸入的模型,並且讓外觀更加平滑。由於上述目標,在建構樂高結構的過程中,需要考量與模型的相似性。我們使用有向距離場、填充率與表面向量作為依據,以選擇適合的樂高磚塊。我們以貪婪策略建構表面區域,而內部構造則使用基本磚塊建構出穩定的內部構造。另外,我們也提出使用轉向磚塊的方式建構出更加接近的結果。最後,我們使用不同的模型及嘗試不同的建構策略來驗證我們的做法。


    Since Lego® bricks was introduced in 1940s, it has become one of the most well-known brick systems. In computer science, there are many researches showing that we can automatically generate lego structures in both 2D and 3D aspects. However, former researches mainly focus on basic bricks, which only uses basic bricks for construction. The final results usually have a voxelized appearance. We propose a method to improve model similarity and smoothness by using a variety of slope bricks. To construct a lego model that looks like the input model, we need to consider the similarity between brick and partial surface of the model. We use signed distance field, fill rate, and surface normal to determine the best bricks for the partial surface. Because total possible lego brick combinations are growing exponentially, it’s very hard to test all possible combinations. We use a greedy strategy to generate our results. For inner area, we use the core blocks and basic bricks to generate the inner structure. Finally, we use a random start strategy and genetic algorithm to evaluate our performance. We also use variable models as our input to test our method.

    第一章 緒論 1
    第二章 相關研究 4
    2.1 樂高結構的表示方法 4
    2.2 建構組合方式的演算法 6
    2.3 風格化對應 7
    第三章 具平滑外觀的樂高建構 8
    3.1 主要目標 8
    3.2 主要挑戰 8
    3.3 主要差異 9
    3.4 使用磚組 9
    第四章 方法 12
    4.1 系統架構 12
    4.2 資料結構表示 14
    4.3 模型前處理 15
    4.3.1 表面區域 16
    4.3.2 描述子 17
    4.4 表面建構演算法 19
    4.4.1 最佳磚選擇 20
    4.4.2 斜磚選擇 20
    4.5 鋸齒消除 24
    4.6 隨機起始法 28
    4.7 基因演算法 29
    4.8 多方向堆積 30
    4.9 結構穩定度 33
    第五章 結果與限制 34
    5.1 策略比較 34
    5.2 不同種類模型結果 39
    5.3 系統環境 45
    5.4 系統限制 45
    第六章 結論與未來展望 46
    6.1 結論 46
    6.2 未來展望 47
    參考文獻 48
    附錄A:其他立體模型結果 52
    附錄B:其他立體模型分數比較 54
    附錄C:平面模型分數比較 56

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