點雲建模技術

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：7

、訪客IP：18.223.205.84

姓名

許晴然(Cing-Ran Syu) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

點雲建模技術
(Modeling Technology for Point Cloud)

相關論文

★ 用於牙齒頻譜的多點量測之高光譜系統	★ 結合全像光學元件的微型化數位全像顯微鏡
★ 隨讀取位置改變之多頁繞射疊加訊號之相位誤差容忍度分析	★ 記錄於倒數空間高繞射效率之全像儲存系統
★ 具有角度放大功能之近眼顯示器全像光導	★ 提升近眼顯示器光學效率之輸入影像頻譜分布技術

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

本論文的研究涉及點雲萃取和自動建模兩個主要方面。在點雲萃取方面，我們面臨了高維度數據、噪音和缺失數據以及不規則性和無序性等困難。為了解決這些問題，我們利用機器學習的方法從點雲數據中提取特徵，並進行點雲的分類、分割和重建等任務。機器通過學習的方式對數據進行特徵提取，我們能夠有效地處理高維度的數據。
此外，本論文還提出了自動建模技術，該技術結合了點雲的分割、主值分析和傅立葉分析等方法。通過對點雲進行分割，我們可以將點雲數據中的不同部分區分開來，以便更好地進行後續的建模操作。同時，利用主值分析的方法，我們可以對部件整體的法向量進行提取和分析，從而獲得更準確的三維模型，並利用傅立葉分析解決主值分析無法區分的形狀。這些技術的結合使我們能夠從點雲數據中提取物體的幾何訊息並自動建模，最終生成三維模型。

摘要(英)

This thesis involves point cloud extraction and automatic modeling. In point cloud extraction, we face difficulties such as high-dimensional data, noise and missing data, and irregularity and disorder. In order to solve these problems, we use the method of machine learning to extract features from point cloud, and perform tasks such as classification, and segmentation on point cloud. Machines extract features from data through learning, enabling us to effectively process high-dimensional data.
In addition, this research proposes an automatic modeling technique that combines methods such as segmentation of point clouds and principal components analysis. By segmenting the point cloud, we can distinguish different parts of the point cloud for better subsequent modeling operations. Using the method of principal value analysis, we can extract and analyze the normal vector of the whole part, so as to obtain a more accurate 3D model. The combination of these technologies enables us to extract object information from point cloud data and automatically model, and finally generate a 3D model.

關鍵字(中)

★ 點雲
★ 建模

關鍵字(英)

論文目次

中文摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章緒論 1
1-1 研究背景與動機 1
1-2 相關論文回顧 2
第二章基礎原理 6
2-1 點雲資料 6
2-2 人工智慧 8
2-3數值分析 20
2-3-1 主成分分析法 20
2-3-2 展開式 22
第三章基於PointNet2之點雲萃取 24
3-1 數據集與其特性 24
3-2 網路架構 26
3-3 點雲萃取流程 29
3-3-1 物件分類 30
3-3-2 部件分割 34
3-4結論 35
第四章基於基本圖形之建模 37
4-1表面建模流程 37
4-2演算法流程圖 38
4-2-1 圖形分類 39
4-2-2 表面形狀分析 41
4-2-3 OBJ檔案編寫 47
4-2-4 基本圖形與建模後的量化分析 48
4-3 物件建模實例 52
4-3-1 桌子之建模 52
4-3-2 椅子之建模 59
第五章結論 64
參考文獻 66
中英文名詞對照表 70

參考文獻

S. Rokhsaritalemi, A. S. Niaraki, and S.M. Choi, “A Review on Mixed Reality: Current Trends, Challenges and Prospects,” Appl. Sci. 10(2), 636-661 (2020).
C. R. Qi, L. Yi, S. Hao, and L. J. Guibas, “PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,” presented at the 〖31〗^st International Conference on Neural Information Processing Systems, California, United State, 4-9 December 2017.
C. Shang, J. Fu, J. Feng, Z. Lin, and B. Li, “Effective re-parameterization and GA based knot structure optimization for high quality t-spline surface fitting,” Computer Methods in Applied Mechanics and Engineering 351(1), 836-859 (2019).
D. Boltcheva and B. Lévy, “Surface reconstruction by computing restricted voronoi cells in parallel,” Computer Aided Design 90, 123-134 (2017).
M. S. Abdel-Wahab, A. S. Hussein, I. Taha and M. S. Gabel, “An enhanced algorithm for surface reconstruction from a cloud of points,” presented at GVIP, Cairo, Egypt, 19-21 September 2005.
W. Wang, T. Su, H. Liu, X. Li, Z. Jia, L. Zhou, Z. Song, and M. Ding, “Surface reconstruction from unoriented point clouds by a new triangle selection strategy,” Computer & Graphics 84, 144-159 (2019).
X. Ren, L. Lyu, X. He, W. Cao, Z. Yang, B. Sheng, Y. Zhang, and E. Wu, “Biorthogonal wavelet surface reconstruction using partial integrations,” Computer Graphics Forum 37(7), 13-24 (2018).
Y. Tang and J. Feng, “Multi-scale surface reconstruction based on a curvature-adaptive signed distance field,” Computer & Graphics 70, 28-38 (2017).
H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle, “Surface reconstruction from unorganized points,” Computer & Graphics 26(2), 71-78 (1992).
B. Curless and M. Levoy, “A volumetric method for building complex models from range images,” presented at the 〖23〗^rd Annual Conference on Computer Graphics and Interactive Techniques, New York, United State, 1 August 1996.
F. Calakli and G. Taubin, “SSD: smoothed signed distance surface reconstruction,” Computer Graphics Forum 30(7), 1993-2002 (2011)
J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, and T. R. Evans, “Reconstruction and representation of 3D objects with radial basis functions,” presented at the 〖28〗^th Annual Conference on Computer Graphics and Interactive Techniques, New York, United State, 1 August 2001.
X. Y. Liu, H. Wang, C. S. Chen, Q. Wang, X. Zhou, and Y. Wang, “Implicit surface reconstruction with radial basis functions via PDEs,” Engineering Analysis with Boundary Elements 110, 95-103 (2020).
J. Yang, Z. Wang, C. Zhu, and Q. Peng, “Implicit surface reconstruction with radial basis functions,” presented at International Conference on Computer Vision and Computer Graphics 21, 5-12 (2008).
Y. Ohtake, A. G. Belyaev, M. Alexa, G. Turk and H. Seidel, “Multi-level partition of unity implicits,” ACM Transactions on Graphics 22(3), 463-470 (2003).
M. Kazhdan, M. Bolitho, and H. Hoppe, “Poisson surface reconstruction,” presented at the 4^th Eurographics Symposium on Geometry Processing, Goslar, Germany, 26 June 2006.
M. Kazhdan and H. Hoppe, “Screened poisson surface reconstruction,” ACM Transactions on Graphics 32(3), 1-13 (2013).
J. Manson, G. Petrova, and S. Schaefer, “Streaming surface reconstruction using wavelets,” presented at the Symposium on Geometry Processing, Goslar, Germany, 15-17 July 2009.
X. Ren L. Lyu, X. He, W. Cao, Z. Yang, B. Sheng, Y. Zhang, and E. Wu, “Biorthogonal wavelet surface reconstruction using partial integrations,” Computer Graphics Forum 37, 13-24 (2018).
W. Wang, T. Su, H. Liu, X. Li, Z. Jia, L. Zhou, Z. Song, and M. Ding, “Surface reconstruction from unoriented point clouds by a new triangle selection strategy,” Computer & Graphics 84, 144-159 (2019).
M. Pan, W. Tong, and F. Chen, “Phase-field guided surface reconstruction based on implicit hierarchical b-splines,” Computer Aided Geometric Design 52, 154-169 (2017).
H. Li, Y. Li, R. Yu, J. Sun, and J. Kim, “Surface reconstruction from unorganized point clouds with l_0 gradient minimization,” Computer Vision and Image Understanding 169(5), 108-118 (2018).
T. Groueix, M. Fisher, V. Kim, B. Russel, and M. Aubry, “AtlasNet: a paper-mâché approach to learning 3D surface generation,” presented at 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 18-23 June 2018.
F. Williams, T. Schneider, C. Silva, D. Zorin, J. Bruna, and D. Panozzo, “Deep geometric prior for surface reconstruction,” presented at 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 15-20 June 2019.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “DeepSDF: learning continuous signed distance functions for shape representation,” presented at 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 15-20 June 2019.
X. Y. Liu, H. Wang, C. S. Chen, Q. Wang, X. Zhou, and Y. Wang, “Implicit surface reconstruction with radial basis functions via PDEs,” Engineering Analysis with Boundary Elements 110, 95-103 (2020).
M. Atzmon and Y. Lipman, “SAL: sign agnostic learning of shapes from raw data,” presented at 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 13-19 June 2020.
Z. Mi, Y. Luo, and W. Tao, “SSRNet: Scalable 3D Surface Reconstruction Network,” presented at 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 13-19 June 2020.
Z. Huang, Y. Wen, Z. Wang, J. Ren, and K. Jia, “Surface Reconstruction from Point Clouds: A Survey and a Benchmark,” arXiv preprint arXiv:2205.02413 (2022).
C. Chao, M. Preda, and T. Zaharia, “3D Point Cloud Compression: A Survey,” presented at 24th International Conference on 3D Web Technology, Los Angeles, United State, 26-28 July 2019.
M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science 349(6245), 255-260 (2015).
K. Das and R. N. Behera, “A survey on machine learning: concept, algorithms and applications,” International Journal of Innovative Research in Computer and Communication Engineering 2(2), 1301-1309 (2017)
I. T. Jolliffe, Principal Component Analysis, 2nd eds. (Springer-Verlag, New NORK, 1986).
O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon 4, 1-41 (2018)
I. T. Joliffe and J. Candima, “Principal component analysis: a review and recent developments,” Philosophical Transactions of the Royal Society a Mathematical, Physical and Engineering Sciences 374(2065), 1-16 (2016)
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao. “3d shapenets: A deep representation for volumetric shapes,” presented at 2015 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 8-10 June 2015.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” presented at 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 21-26 June 2017.
M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz, “Rotation invariant spherical harmonic representation of 3d shape descriptors,” presented at Symposium on Geometry Processing, Aachen, Germany, 23-25 June 2003.
C. R. Qi, H. Su, M. Niessner, A. Dai, M. Yan, and L. J. Guibas, “Volumetric and multi-view CNNs for object classification on 3D data,” presented at 2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 27-30 June 2016.
H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” presented at 2015 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, United State, 8-10 June 2015.

指導教授

余業緯孫慶成

審核日期

2023-8-16

推文