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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/150171
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150171

    Title: 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
    Authors: 黃威
    Huang, Wei
    Contributors: 紀明德
    Chi, Ming-Te
    Huang, Wei
    Keywords: 樂高
    Graphic neural network,
    Date: 2024
    Issue Date: 2024-03-01 13:42:18 (UTC+8)
    Abstract: 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
    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.
    Reference: [1] LEGO® Starry Night. https://www.lego.com/zh-tw/categories/adults-welcome/
    [2] LEGO® Great Wave. https://www.lego.com/zh-tw/categories/adults-welcome/
    [3] LEGO® Wind God and Thunder God Screens. https://toymim.com/review/
    [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/
    [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),
    [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.
    [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.
    [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.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110753159
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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