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研究生:黃威
研究生(外文):Huang, Wei
論文名稱:以圖神經網路將 2.5D 樂高建構映射至平鋪問題之方法
論文名稱(外文):Mapping 2.5D Lego Construction into Tiling Problem with Graph Neural Network
指導教授:紀明德紀明德引用關係
指導教授(外文):Chi, Ming-Te
口試委員:林世勛謝東儒
口試委員(外文):Lin, Shih-SyunHSIEH, TUNG-JU
口試日期:2024-01-29
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:45
中文關鍵詞:樂高圖神經網路超像素問題
外文關鍵詞:LEGOGraphic neural networkSuperpixel
相關次數:
  • 被引用被引用:0
  • 點閱點閱:9
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
樂高公司以積木的多樣性深受大人和小孩喜愛,隨著模型複雜度
的增加,人們對樂高模型的組裝有了更高的要求。以樂高浮雕系列為
例,模型以其高度立體的設計和細緻的細節而聞名,處理複雜的三維
空間和結構問題上的能力,使組裝過程更具挑戰性。
本研究著重在處理樂高浮雕系列的複雜性。在這一過程中,我們
需要克服積木的幾何形狀、分層架構和結構強度等多重挑戰,同時必
須在有限的樂高磚資源下實現豐富多樣的創意。為了解決這些問題,
我們採用了三項關鍵技術:圖像分層、樂高生成技術與相似度量化分
析。首先,透過圖像分層技術,我們得以細緻地將輸入圖像分為前景
和背景,深入切分圖像中的細節,進而突顯更多層次的圖像細節。其
次,我們應用樂高生成技術,在區域內最大化平鋪樂高磚,確保模型
的結構穩固,同時解決超像素問題。最後,我們運用相似度量化分析
演算法來比較生成的模型和原始輸入圖像的相似度,全面評估和比較
各種模型的表現。這項分析不僅確保了模型的忠實還原,同時也為我
們提供了改進的空間,以進一步提高模型的精確度和真實感。這些技
術的綜合應用為樂高浮雕系列的設計提供了全新的方法和解決方案,
進一步滿足了樂高愛好者的多樣性。
The LEGO company’s diverse building blocks are loved by both adults
and children. As models become more complex, there are higher demands
for assembling LEGO models. For example, the LEGO relief series, known
for its intricate three-dimensional design , presents challenges in handling
complex spatial and structural issues.
Our study focuses on addressing the complexity of the LEGO relief series. We employ three key technologies: image segmentation, LEGO generation techniques, and similarity quantification analysis. Image segmentation divides input images into foreground and background, emphasizing more
layers of detail. LEGO generation techniques maximize brick placement for
structural stability while solving the superpixel problem. Similarity quantification analysis ensures faithful reproduction of models and provides room
for improvement.By applying these technologies, we offer new methods and
solutions for designing LEGO relief series, catering to the diverse interests of
LEGO enthusiasts.
致謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
第一章 緒論 1
1.1 研究動機與目的 1
1.2 問題描述 3
1.3 論文貢獻 4
第二章 相關研究 5
2.1 平鋪問題 5
2.2 分層架構 6
2.3 超像素問題 6
2.4 最佳化 7
第三章 研究方法與步驟 9
3.1 資料預處理 10
3.1.1 分層 (Layer) 10
3.1.2 額外分層架構 14
3.1.3 資料預處理 14
3.2 風格化 15
3.3 平鋪問題 16
3.4 顏色對應 16
3.4.1 顏色量化分析 19
3.5 量化分析 21
3.6 分治法 (divided-and-conquer) 22
第四章 實驗與結果 24
4.1 實驗數據比較 25
4.1.1 不同 ColorMapping 方法比較 25
4.1.2 K 值選擇與比較 26
4.1.3 組裝結果大小 27
4.1.4 圖形位移 28
4.2 結果 28
4.3 限制 36
第五章 結論與未來展望 39
5.1 結論 39
5.2 未來展望 40
附錄 A 41
參考文獻 43
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article/details-of-van-gogh-starry-night.
[2] LEGO® Great Wave. https://www.lego.com/zh-tw/categories/adults-welcome/
article/how-we-made-the-lego-great-wave.
[3] LEGO® Wind God and Thunder God Screens. https://toymim.com/review/
lego-store-narita-airport-report-2020-01.
[4] A. Rivers, T. Igarashi, and F. Durand, “2.5 d cartoon models,” ACM Transactions
on Graphics (TOG), vol. 29, no. 4, pp. 1–7, 2010.
[5] H. Xu, K. H. Hui, C.-W. Fu, and H. Zhang, “Tilingnn: learning to tile with selfsupervised graph neural network,” arXiv preprint arXiv:2007.02278, 2020.
[6] LEGO® Brick Modified . https://rebrickable.com/parts/87087/
brick-special-1-x-1-with-stud-on-1-side/.
[7] R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust
monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),
2020.
43
[8] R. Ranftl, A. Bochkovskiy, and V. Koltun, “Vision transformers for dense prediction,” ArXiv preprint, 2021.
[9] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive
survey on graph neural networks,” IEEE transactions on neural networks and
learning systems, vol. 32, no. 1, pp. 4–24, 2020.
[10] L. Sacht, “Structure-aware bottle cap art,” Computers & Graphics, vol. 107, pp.
277–288, 2022.
[11] J. Allebach and P. W. Wong, “Edge-directed interpolation,” in Proceedings of 3rd
IEEE International Conference on Image Processing, vol. 3. IEEE, 1996, pp.
707–710.
[12] R. E. Carlson and F. N. Fritsch, “Monotone piecewise bicubic interpolation,”
SIAM journal on numerical analysis, vol. 22, no. 2, pp. 386–400, 1985.
[13] 翁瑋辰, “具樂高平滑化之影像樂高風格化技術,” 2019.
[14] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of
the IEEE international conference on computer vision, 2017, pp. 2961–2969.
[15] 王祥宇, “以圖神經網路將二維樂高建構映射至平鋪問題之方法,” 2022.
[16] P. Lei, S. Xu, and S. Zhang, “An art-oriented pixelation method for cartoon images,” The Visual Computer, pp. 1–13, 2023.
[17] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable
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44
[18] R. Gower, A. Heydtmann, and H. Petersen, “Lego: Automated model construction,” 1998.
[19] M.-H. Kuo, Y.-E. Lin, H.-K. Chu, R.-R. Lee, and Y.-L. Yang, “Pixel2brick: Constructing brick sculptures from pixel art,” in Computer Graphics Forum, vol. 34,
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[20] S.-J. Luo, Y. Yue, C.-K. Huang, Y.-H. Chung, S. Imai, T. Nishita, and B.-Y.
Chen, “Legolization: Optimizing lego designs,” ACM Transactions on Graphics
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[21] H. Xu, K.-H. Hui, C.-W. Fu, and H. Zhang, “Computational lego technic design,”
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and I. Drori, “Image2lego: customized lego set generation from images,” arXiv
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[25] LEGO® Brick. https://brickhub.org.
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