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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93656完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林之謙 | zh_TW |
| dc.contributor.advisor | Jacob J. Lin | en |
| dc.contributor.author | 洪梓航 | zh_TW |
| dc.contributor.author | Ang Chi-Hang | en |
| dc.date.accessioned | 2024-08-07T16:13:22Z | - |
| dc.date.available | 2024-08-08 | - |
| dc.date.copyright | 2024-08-07 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-26 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93656 | - |
| dc.description.abstract | 點雲相比於影像，具備三維的特徵且更能反映現場真實狀況，能幫助許多工程上的應用，例如進度管理、品質管理等等。在點雲的應用上，點雲套合是一件重要的前置任務。現今，我們需要將點雲手動整合到統一的座標系統，這是一項重複性且耗費人力的工作，而自動化的點雲套合受困于難以在低重叠、跨源頭的點雲取得對應特徵，進而影響其套合成效。本研究基於在建築環境内多是結構線的緣由，提出一個以線特徵進行點雲自動套合的框架，此框架分由4個模組組成，包括線的分割 (Line Extraction)，線特徵的萃取與配對 (Line Feature Extraction and Matching)，離群值檢驗 (Outlier Pruning)和線的套合模組 (Line-based Registration)。此外，我們設計了一個深度神經網路（LineNet），以進行線的特徵萃取和配對。我們的線特徵套合框架在點雲套合的資料集（3DMatch）上達到26.46%的套合成功率，超過目前最好的線特徵套合模型。最後我們也對使用線特徵進行套合的重要因素進行討論，其中包括針對各個模組的品質因子的討論。 | zh_TW |
| dc.description.abstract | Point clouds have a broad range of applications in construction monitoring and management, such as progress monitoring and quality inspection. Nonetheless, point cloud registration is a crucial task before most point cloud applications. Currently, the alignment of point clouds into the same global coordinate system largely requires manual work, which is repetitive and labor-intensive, and the automated point cloud registration suffers from finding correct correspondences between cross-source or low overlapping point clouds. The salient and abundant line geometry in man-made structures contains the potential to automate the registration process. This research aims to perform point cloud registration by using line geometry as a matching feature. We propose a line-based point cloud registration framework and design the LineNet, a line feature encoding network to perform the feature extraction and matching. We experimented with the proposed framework on the representative 3DMatch dataset, and the overall registration recall is 26.46%, which have great improvement against the latest line-based method. Some discussions on the important factors to perform a good line-based point cloud registration have been made. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-07T16:13:22Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-07T16:13:22Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 3
Abstract 5 Contents 7 List of Figures 11 List of Tables 13 Chapter 1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Challenges on Point Cloud Registration . . . . . . . . . . . . . . . . 2 1.3 Research Gap and Objectives . . . . . . . . . . . . . . . . . . . . . 4 1.4 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 2 Related Work 7 2.1 Direct Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Transform Registration . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Hand-crafted Feature Extraction . . . . . . . . . . . . . . . . . . . 10 2.2.2 Learning-based Feature Extraction . . . . . . . . . . . . . . . . . . 11 2.2.3 Line and Plane Features . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 End-to-end Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Chapter 3 Methodology 17 3.1 Line Extraction Module . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Line Feature Extraction and Matching Module . . . . . . . . . . . . 21 3.2.1 Line Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2.2 Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.3 Matching Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.4 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2.5 LineNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2.6 PluckerNet Modification . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Outlier Pruning Module . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 RANSAC-based Line Registration Module . . . . . . . . . . . . . . 30 3.4.1 Minimal 2-line Solver . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 Minimal 3-line Solver . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4.3 Random Sampling Consensus (RANSAC) . . . . . . . . . . . . . . 36 Chapter 4 Results 39 4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.1.1 3DMatch and UrbanDataset . . . . . . . . . . . . . . . . . . . . . . 39 4.1.2 Dataset Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2 Line Extraction Module . . . . . . . . . . . . . . . . . . . . . . . . 42 4.3 Line Feature Extraction and Matching accuracy . . . . . . . . . . . . 44 4.4 RANSAC-based Line Registration Module . . . . . . . . . . . . . . 47 Chapter 5 Discussion 51 5.1 Experiment on the Matching Accuracy Requirement . . . . . . . . . 51 5.2 Comparison between the 2-line Solver and 3-line Solver . . . . . . . 53 5.3 The Performance of LineNet on Low Overlapping Ratio Point Cloud Pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.4 Comparison with the State-of-the-art Point-based Methods . . . . . . 55 5.5 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Chapter 6 Conclusion 59 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 References 63 | - |
| dc.language.iso | en | - |
| dc.subject | 線特徵 | zh_TW |
| dc.subject | 建築區域 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 點雲套合 | zh_TW |
| dc.subject | point cloud registration | en |
| dc.subject | built environment | en |
| dc.subject | line feature | en |
| dc.subject | deep learning | en |
| dc.title | 應用深度線特徵於建築環境内之點雲套合 | zh_TW |
| dc.title | Using Deep Line Feature for Point Cloud Registration in Built Environment | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 韓仁毓;紀宏霖 | zh_TW |
| dc.contributor.oralexamcommittee | Jen-Yu Han;Hung-Lin Chi | en |
| dc.subject.keyword | 點雲套合,建築區域,線特徵,深度學習, | zh_TW |
| dc.subject.keyword | point cloud registration,built environment,line feature,deep learning, | en |
| dc.relation.page | 69 | - |
| dc.identifier.doi | 10.6342/NTU202402124 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-07-29 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2026-07-31 | - |
| 顯示於系所單位： | 土木工程學系 | |
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