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| 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
黃威
Huang, Wei |
| Keywords: | 樂高
圖神經網路
超像素問題
LEGO
Graphic neural network,
Superpixel |
| 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
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. |
| Reference: | [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
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2020.
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[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.
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[24] M.-R. Huang and R.-R. Lee, “Pixel art color palette synthesis,” in Information
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[25] LEGO® Brick. https://brickhub.org. |
| Description: | 碩士
國立政治大學
資訊科學系
110753159 |
| 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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