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    題名: 以圖神經網路將 2.5D 樂高建構映射至平鋪問題之方法
    Mapping 2.5D Lego Construction into Tiling Problem with Graph Neural Network
    作者: 黃威
    Huang, Wei
    貢獻者: 紀明德
    Chi, Ming-Te
    黃威
    Huang, Wei
    關鍵詞: 樂高
    圖神經網路
    超像素問題
    LEGO
    Graphic neural network,
    Superpixel
    日期: 2024
    上傳時間: 2024-03-01 13:42:18 (UTC+8)
    摘要: 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.
    參考文獻: [1] LEGO® Starry Night. https://www.lego.com/zh-tw/categories/adults-welcome/
    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
    effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE
    conference on computer vision and pattern recognition, 2018, pp. 586–595.
    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,
    no. 7. Wiley Online Library, 2015, pp. 339–348.
    [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
    (TOG), vol. 34, no. 6, pp. 1–12, 2015.
    [21] H. Xu, K.-H. Hui, C.-W. Fu, and H. Zhang, “Computational lego technic design,”
    arXiv preprint arXiv:2007.02245, 2020.
    [22] K. Lennon, K. Fransen, A. O’Brien, Y. Cao, M. Beveridge, Y. Arefeen, N. Singh,
    and I. Drori, “Image2lego: customized lego set generation from images,” arXiv
    preprint arXiv:2108.08477, 2021.
    [23] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image
    processing, vol. 13, no. 4, pp. 600–612, 2004.
    [24] M.-R. Huang and R.-R. Lee, “Pixel art color palette synthesis,” in Information
    Science and Applications. Springer, 2015, pp. 327–334.
    [25] LEGO® Brick. https://brickhub.org.
    描述: 碩士
    國立政治大學
    資訊科學系
    110753159
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110753159
    資料類型: thesis
    顯示於類別:[資訊科學系] 學位論文

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