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題名 處理含有雜訊之點雲骨架的生成
Dealing with Noisy Data for the Generation of Point Cloud Skeletons
作者 林逸芃
Lin, Yi Peng
貢獻者 徐國偉
Hsu, Kuo Wei
林逸芃
Lin, Yi Peng
關鍵詞 雜訊
點雲
骨架
Noise
Point cloud
Skeleton
日期 2013
上傳時間 6-Aug-2014 11:47:19 (UTC+8)
摘要 一個視覺物體或一個三維模型的骨架，是一種可以揭示該物體或模型的拓樸結構的呈現方式，因此骨架可以被應用在諸多場合當中，例如形狀分析和電腦動畫。近年來，有許多針對從一個物體當中抽取骨架的研究工作。然而，大多數的研究著重於完整和乾淨的資料（儘管這些研究當中，有一些有將缺失值考慮在內），但在實務上，我們經常要處理不完整和不潔淨的資料，就像資料裡面可能有缺失值和雜訊。在本論文中，我們研究雜訊處理，而且我們將焦點放在針對帶有雜訊的點雲資料進行前置處理，以便生成相應物體的骨架。在我們提出的方法當中，我們首先識別可能帶有雜訊的資料點，然後降低雜訊值的影響。為了識別雜訊，我們將監督式學習用在以密度和距離作為特徵的資料上。為了降低雜訊值的影響，我們採用三角形表面和投影。這個前置處理方法是有彈性的，因為它可以搭配任何能夠從點雲資料當中抽取出物體的骨架的工具。我們用數個三維模型和多種設定進行實驗，而結果顯示本論文所提出的前置處理方法的有效性。與未經處理的模型（也就是原始模型加上雜訊）相比，在從帶有雜訊的點雲資料當中產生物體的骨架之前，如果我們先使用本論文所提出的前置處理方法，那麼我們可以得到一個包含更多原來的物體的拓撲特徵的骨架。我們的貢獻如下：第一，我們展示了機器學習可以如何協助電腦圖學。第二、針對雜訊識別，我們提出使用距離和密度做為學習過程中要用的特徵。第三、我們提出使用三角表面和投影，以減少在做雜訊削減時所需要花費的時間。第四、本論文提出的方法可以用於改進三維掃描。
The skeleton of a visual object or a 3D model is a representation that can reveal the topological structure of the object or the model, and therefore it can be used in various applications such as shape analysis and computer animation. Over the years there have been many studies working on the extraction of the skeleton of an object. However, most of those studies focused on complete and clean data (even though some of them took missing values into account), while in practice we often have to deal with incomplete and unclean data, just as there might be missing values and noise in data. In this thesis, we study noise handling, and we put our focus on preprocessing a noisy point cloud for the generation of the skeleton of the corresponding object. In the proposed approach, we first identify data points that might be noise and then lower the impact of the noisy values. For identifying noise, we use supervised learning on data whose features are density and distance. For lowering the impact of the noisy values, we use triangular surfaces and projection. The preprocessing method is flexible, because it can be used with any tool that can extract skeletons from point clouds. We conduct experiments with several 3D models and various settings, and the results show the effectiveness of the proposed preprocessing approach. Compared with the unprocessed model (which is the original model with the added noise), if we apply the proposed preprocessing approach to a noisy point cloud before using a tool to generate the skeleton, we can obtain a skeleton that contains more topological characteristics of the model. Our contributions are as follows: First, we show how machine learning can help computer graphics. Second, we propose to use distance and density as features in learning for noise identification. Third, we propose to use triangular surfaces and projection to save execution time in noise reduction. Fourth, the proposed approach could be used to improve 3D scanning.
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描述 碩士
國立政治大學
資訊科學學系
101753025
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101753025
資料類型 thesis
dc.contributor.advisor 徐國偉zh_TW
dc.contributor.advisor Hsu, Kuo Weien_US
dc.contributor.author (Authors) 林逸芃zh_TW
dc.contributor.author (Authors) Lin, Yi Pengen_US
dc.creator (作者) 林逸芃zh_TW
dc.creator (作者) Lin, Yi Pengen_US
dc.date (日期) 2013en_US
dc.date.accessioned 6-Aug-2014 11:47:19 (UTC+8)-
dc.date.available 6-Aug-2014 11:47:19 (UTC+8)-
dc.date.issued (上傳時間) 6-Aug-2014 11:47:19 (UTC+8)-
dc.identifier (Other Identifiers) G0101753025en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/68267-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 101753025zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 一個視覺物體或一個三維模型的骨架，是一種可以揭示該物體或模型的拓樸結構的呈現方式，因此骨架可以被應用在諸多場合當中，例如形狀分析和電腦動畫。近年來，有許多針對從一個物體當中抽取骨架的研究工作。然而，大多數的研究著重於完整和乾淨的資料（儘管這些研究當中，有一些有將缺失值考慮在內），但在實務上，我們經常要處理不完整和不潔淨的資料，就像資料裡面可能有缺失值和雜訊。在本論文中，我們研究雜訊處理，而且我們將焦點放在針對帶有雜訊的點雲資料進行前置處理，以便生成相應物體的骨架。在我們提出的方法當中，我們首先識別可能帶有雜訊的資料點，然後降低雜訊值的影響。為了識別雜訊，我們將監督式學習用在以密度和距離作為特徵的資料上。為了降低雜訊值的影響，我們採用三角形表面和投影。這個前置處理方法是有彈性的，因為它可以搭配任何能夠從點雲資料當中抽取出物體的骨架的工具。我們用數個三維模型和多種設定進行實驗，而結果顯示本論文所提出的前置處理方法的有效性。與未經處理的模型（也就是原始模型加上雜訊）相比，在從帶有雜訊的點雲資料當中產生物體的骨架之前，如果我們先使用本論文所提出的前置處理方法，那麼我們可以得到一個包含更多原來的物體的拓撲特徵的骨架。我們的貢獻如下：第一，我們展示了機器學習可以如何協助電腦圖學。第二、針對雜訊識別，我們提出使用距離和密度做為學習過程中要用的特徵。第三、我們提出使用三角表面和投影，以減少在做雜訊削減時所需要花費的時間。第四、本論文提出的方法可以用於改進三維掃描。zh_TW
dc.description.abstract (摘要) The skeleton of a visual object or a 3D model is a representation that can reveal the topological structure of the object or the model, and therefore it can be used in various applications such as shape analysis and computer animation. Over the years there have been many studies working on the extraction of the skeleton of an object. However, most of those studies focused on complete and clean data (even though some of them took missing values into account), while in practice we often have to deal with incomplete and unclean data, just as there might be missing values and noise in data. In this thesis, we study noise handling, and we put our focus on preprocessing a noisy point cloud for the generation of the skeleton of the corresponding object. In the proposed approach, we first identify data points that might be noise and then lower the impact of the noisy values. For identifying noise, we use supervised learning on data whose features are density and distance. For lowering the impact of the noisy values, we use triangular surfaces and projection. The preprocessing method is flexible, because it can be used with any tool that can extract skeletons from point clouds. We conduct experiments with several 3D models and various settings, and the results show the effectiveness of the proposed preprocessing approach. Compared with the unprocessed model (which is the original model with the added noise), if we apply the proposed preprocessing approach to a noisy point cloud before using a tool to generate the skeleton, we can obtain a skeleton that contains more topological characteristics of the model. Our contributions are as follows: First, we show how machine learning can help computer graphics. Second, we propose to use distance and density as features in learning for noise identification. Third, we propose to use triangular surfaces and projection to save execution time in noise reduction. Fourth, the proposed approach could be used to improve 3D scanning.en_US
dc.description.tableofcontents CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Main framework 5
1.3 Contributions 7
1.4 Organization 9
CHAPTER 2 BACKGROUND 10
2.1 Skeleton extraction 10
2.2 Point cloud 15
2.3 Noise handling 15
CHAPTER 3 THE PROPOSED APPROACH 19
3.1 The preprocessing method 19
3.2 The additive noise model 21
3.3 Noise identification 25
3.4 Noise reduction 29
CHAPTER 4 THE EXPERIMENTS 38
4.1 Overview 38
4.2 1 Stdev, 10% noisy values, using Support Vector Machine 41
4.3 1 Stdev, 10% noisy values, using Naïve Bayes 44
4.4 1 Stdev, 10% noisy values, using Classification Tree 48
4.5 1 Stdev, 20% noisy values, using Support Vector Machine 52
4.6 1 Stdev, 20% noisy values, using Naïve Bayes 56
4.7 1 Stdev, 20% noisy values, using Classification Tree 60
4.8 2 Stdev, 10% noisy values, using Support Vector Machine 64
4.9 2 Stdev, 10% noisy values, using Naïve Bayes 65
4.10 2 Stdev, 10% noisy values, using Classification Tree 66
4.11 2 Stdev, 20% noisy values, using Support Vector Machine 66
4.12 2 Stdev, 20% noisy values, using Naïve Bayes 67
4.13 2 Stdev, 20% noisy values, using Classification Tree 68
4.14 Other experiments---pair models 69
4.15 Discussions 70
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 72
REFERENCES 75		zh_TW
dc.format.extent 7649868 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101753025en_US
dc.subject (關鍵詞) 雜訊zh_TW
dc.subject (關鍵詞) 點雲zh_TW
dc.subject (關鍵詞) 骨架zh_TW
dc.subject (關鍵詞) Noiseen_US
dc.subject (關鍵詞) Point clouden_US
dc.subject (關鍵詞) Skeletonen_US
dc.title (題名) 處理含有雜訊之點雲骨架的生成zh_TW
dc.title (題名) Dealing with Noisy Data for the Generation of Point Cloud Skeletonsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] Junjie Cao, Tagliasacchi A., Olson M., and Hao Zhang. 2010. “Point Cloud Skeletons via
Laplacian Based Contraction,” Shape Modeling International Conference (SMI): 187-197.
[2] S. Wang, J. Wu, M. Wei, and X. Ma. 2012. “Robust curve skeleton extraction for vascular
structures,” Graphical Models 74(4):109-120.
[3] M. Raptis, D. Kirovski, and H. Hoppe. 2011. “Real-time classification of dance gestures
from skeleton animation,” Eurographics Symposium on Computer Animation: 147-156.
[4] R. Schnabel, R. Wessel, R. Wahl, and R. Klein. 2008. “Shape Recognition in 3D
Point-Clouds,” The International Conference in Central Europe on Computer Graphics,
Visualization and Computer Vision. Vol.2.
[5] Aleksey G., Vladimir G. K., and Thomas Funkhouser. 2009. “Shape-based recognition of
3D point clouds in urban environments,” IEEE 12th International Conference on
Computer Vision: 2154-2161.
[6] Alexander V., Roman S., and Konrad Schindler. 2012. “Implicit shape models for object
detection in 3d point clouds,” ISPRS Annals of the Photogrammetry, Remote Sensing and
Spatial Information Sciences. Volume I-3.
[7] Marco Livesu, Fabio Guggeri, and Riccardo Scateni. 2012. “Reconstructing the
Curve-Skeletons of 3D Shapes Using the Visual Hull,” IEEE Transactions on
Visualization and Computer Graphics 18(11): 1891-1901.
[8] J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, and
T. R. Evans. 2001. “Reconstruction and representation of 3D objects with radial basis
functions,” SIGGRAPH `01 Proceedings of the 28th annual conference on Computer
graphics and interactive techniques: 67-76, 2001.
[9] Michael W, Philipp J., Qixing H., Martin B., and Leonidas Guibas. 2007.
“Reconstruction of Deforming Geometry from Time-Varying Point Clouds,” SGP `07
Proceedings of the fifth Eurographics symposium on Geometry processing: 49-58.
[10] D. F. Lu, H. K. Zhao, M. Jiang, S. L. Zhou, and T. Zhou. 2005 “A Surface
Reconstruction Method for Highly Noisy Point Clouds,” Variational, Geometric, and
Level Set Methods in Computer Vision: 283-294
[11] N. J. Mitra, and A. Nguyen. 2003. “Estimating surface normals in noisy point cloud data,”
SCG `03 Proceedings of the nineteenth annual symposium on Computational geometry:
322-328.
[12] J. C. Carr, R. K. Beatson, T. J. Mitchell, W. R. Fright, B. C. McCallum, and
B. C. McCallum. 2003. “Smooth surface reconstruction from noisy range data,”
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