深度學習應用於CAD模型搜索之技術研究

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姓名

陳柏翰(Bo-Han Chen) 查詢紙本館藏

畢業系所

能源工程研究所

論文名稱

深度學習應用於CAD模型搜索之技術研究

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摘要(中)

在先進的產業中，建立一套CAD模型資料庫並藉由CAD模型搜索，減少產品之開發時間之應用已相當普遍。然而，隨著CAD模型機構設計的複雜化，與CAD模型種類與數量的增加，藉由人工方式進行CAD模型搜索變得越來越困難。本研究的主要目的是開發應用於CAD模型資料庫上的計算CAD模型多視角影像演算法與深度學習影像辨識演算法，將CAD模型搜索問題定義為影像辨識問題，訓練一個自動化分類CAD模型的深度學習神經網路。在本研究中，建立了一個總數量為360個，90分類的CAD模型資料庫，其中訓練用CAD模型有90個，測試用CAD模型有270個，並利用計算CAD模型多視角影像演算法，建立一套完整的深度學習數據集。深度學習方面則是利用VGG16神經網路進行遷移學習、超參數調優與合併不同視角的深度學習模型等方式，最佳化深度學習模型。本研究最主要的貢獻為利用深度學習演算法，成功的分類CAD模型，並且藉由組合不同視角的深度學習模型，提高辨識的正確率。

摘要(英)

Industries nowadays, it becomes mandatory for establishing CAD model database for the purpose of reducing development time of the product design. The database will ease the user to search the similarity between the developing and database models. The time needed for searching the similarity is corresponding to the complexity and various types of CAD models. Therefore, the purpose of this study is to provide a method and procedures to reduce the time needed for CAD model searching. This task can be achieved by combining a multi-view imaging technique for CAD models and a deep learning image classification algorithm. The first technique is for generating an image to represent the distribution of the depth in a view for a CAD model, and then the second technique is employed to apply multi-view image data for the training of the AI model. These two techniques can be combined to classify CAD models automatically based on its similarity. In this study, we provide 360 CAD-model databases and 90 classes generated by using multi-view images and deep learning classification algorithm. The 360 CAD database consists of 90 CAD models for training the AI models and 270 CAD models for testing the AI models. Eventually, using the proposed multi-view imaging algorithm to build a complete deep learning database. For deep learning, it employs VGG16 transfer learning, hyperparameter tuning, and assemble deep learning models from different views to optimize the deep learning models. The main contribution of this study is that we develop a multi-view imaging algorithm to convert a CAD model into images, and successfully classify CAD models and improve the accuracy of the learning by assembling the deep learning models from different views.

關鍵字(中)

★ CAD模型搜索
★ 深度學習
★ 遷移學習

關鍵字(英)

論文目次

目錄
摘要 i
Abstract viii
目錄 ix
圖目錄 xii
表目錄 xiv
第一章緒論 1
1.1前言 1
1.2 文獻回顧 2
1.2.1分區間演算法相關文獻 2
1.2.2深度學習影像辨識相關文獻 2
1.2.3三維物體深度學習相關文獻 3
1.3 研究目的與方法 6
1.3.1 研究目的 6
1.3.2 研究方法 7
1.4 論文架構 9
第二章 3D CAD Model計算多視角影像之方法 11
2.1前言 11
2.2 CAD模型多視角影像演算法整體流程說明 11
2.3 CAD模型前處理與網格化 14
2.3.1 CAD模型尺寸與位置正規化 14
2.3.2 CAD模型網格化 14
2.4像素陣列離CAD網格模型高度計算 16
2.4.1建立像素點陣列 16
2.4.2 XY平面上劃分區間 16
2.4.3像素點隸屬區間計算 20
2.4.4三角網格隸屬區間計算 21
2.4.5像素點離CAD網格模型高度計算 21
2.4.6高度值正規化與記錄像素陣列資料 23
2.5讀取像素陣列資料與輸出PNG影像 23
第三章深度學習影像辨識 28
3.1前言 28
3.2 人工神經網路 28
3.2.1模擬神經元 28
3.2.2全連接神經網路 30
3.3 反向傳播演算法 30
3.3.1神經網路誤差的計算 32
3.3.2 激勵函數 34
3.3.3 誤差函數 36
3.3.4 誤差的反向傳播 36
3.3.5 權重的更新 38
3.3.6 最佳化法 39
3.4 卷積神經網路 40
3.4.1卷積層 42
3.4.2 池化層 44
3.4.3 全連接層 44
3.5 VGG神經網路 49
3.5.1 CNN感受野 49
3.6 遷移學習 51
第四章研究結果與分析 52
4.1 前言 52
4.2 研究設備 52
4.3 計算多視角影像程式操作流程 52
4.3.1計算多視角影像資料 52
4.3.2輸出多視角PNG影像 53
4.3.3計算時間探討 53
4.4自動化腳本程式操作流程 57
4.5 CAD模型總覽 57
4.6深度學習數據集 57
4.6.1訓練用數據集 57
4.6.2測試用數據集 60
4.7 深度學習訓練結果呈現 60
4.7.1上視圖深度學習模型訓練結果 61
4.7.2下視圖深度學習模型訓練結果 67
4.7.3上下視圖CNN模型合併結果 75
4.7.4 AI判斷錯誤模型 76
第五章結論與未來展望 83
5.1 結論 83
5.2 未來展望 84
參考文獻 86

參考文獻

參考文獻
[1] J. Y. Lai, S. H. Shu and Y. C. Huang, “A cell subdivision strategy for r-nearest neighbors computation”, J. Chin. Inst. Eng, Vol. 29, pp. 953–965, 2006.
[2] A. Krizhevsky, I. Sutskever and G. E. Hinton, “Imagenet classification with deep convolutional neural networks”, Advances in Neural Information Processing Systems, Vol. 25, pp. 1097-1105, 2012.
[3] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition.”, International Conference on Learning Representations , 2015.
[4] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, ... and A. Rabinovich, “Going deeper with convolutions”, IEEE Conference On Computer Vision and Pattern Recognition, pp. 1-9, 2015.
[5] K. He, X. Zhang,S. Ren and J. Sun, “Deep residual learning for image recognition”, IEEE Conference on Computer Vision and Pattern Recognition,
pp. 770-778, 2016.
[6] H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition”, IEEE International Conference on Computer Vision, pp.945-953, 2015.
[7] B. T. Phong, “ Illumination for computer generated pictures”, Commun. ACM, pp. 6, 1975.
[8] Q. Yu, C. Yang, H. Fan and H. Wei, “Latent-MVCNN: 3D shape recognition using multiple views from pre-defined or random viewpoints ”, Neural Processing Letters, Vol. 52, pp. 581-602, 2020.
[9] Y. Feng, H. You, X. Zhao and Y. Gao, “Meshnet: mesh neural network for 3D shape representation”, AAAI Conference on Artificial Intelligence, Vol. 33, No. 01, pp. 8279-8286, 2019.
[10] C. R. Qi, H. Su, K. Mo and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation”, IEEE Conference On Computer Vision and Pattern Recognition, pp. 652-660, 2017.
[11] C. Zhang and G. Zhou, “A view-based 3D CAD model reuse framework enabling product lifecycle reuse”, Advances in Engineering Software, Vol. 127, pp. 82-89, 2019.
[12] S. Dong, K. Zou and W. Li, “Fine-grained recognition of 3D shapes based on multi-view recurrent neural network”, International Conference on Machine Learning and Computing, pp. 152-156, 2020.
[13] Y. Feng, Z. Zhang, X. Zhao, R. Ji and Y. Gao, “Gvcnn: group-view convolutional neural networks for 3D shape recognition”, IEEE Conference on Computer Vision and Pattern Recognition, pp 264-272, 2018.
[14] T. Yu, J. Meng and J. Yuan, “Multi-view harmonized bilinear network for 3D object recognition”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 186-194, 2018
[15] Z. Yang and L. Wang, “Learning relationships for multi-view 3D object recognition”, IEEE/CVF International Conference on Computer Vision , pp. 7505-7514, 2019.
[16] J. Jiang, D. Bao, Z.Chen, X. Zhao and Y. Gao, “ MLVCNN: Multi-loop-view convolutional neural network for 3D shape retrieval”, AAAI Conference on Artificial Intelligence, Vol. 33, pp. 8513-8520, 2019.
[17] Rhinoceros , Website:http://www.rhino3d.com, Accessed 18 June 2021.
[18] openNURBS, Website: http://www.rhino3d.com/tw/opennurbs, Accessed 18 June 2021.
[19] PyTorch, Website:https://pytorch.org/, Accessed 18 June 2021.
[20] Numpy, Website:https: https://numpy.org/, Accessed 18 June 2021.
[21] Matplotlib,Website:https://matplotlib.org/, Accessed 18 June 2021.
[22] 曾定璋,深度學習電腦視覺,Website:http://ip.csie.ncu.edu.tw/i09.htm, Accessed 18 June 2021.
[23] 卷積神經網路,Website:https://chtseng.wordpress.com/2017/09/12/%E5%88%9D%E6%8E%A2%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF/,Accessed 18 June 2021.
[24] 動手學深度學習,Website:https://zh.d2l.ai/chapter_introduction/deep-learning-intro.html, Accessed 18 June 2021.

指導教授

賴景義(Jing-Yih Lai)

審核日期

2021-7-27

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