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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 韓仁毓(Jen-Yu Han) | |
| dc.contributor.author | Li-Sheng Chen | en |
| dc.contributor.author | 陳立笙 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:48:06Z | - |
| dc.date.available | 2022-07-28 | |
| dc.date.copyright | 2017-07-28 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-25 | |
| dc.identifier.citation | Abdelhafiz, A., 2009. Integrating digital photogrammetry and terrestrial laser scanning. Ph.D. dissertation, Institute of Geodesy and Photogrammetry, Braunschweig University of Technology, Braunschweig, Germany.
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URL: https://goo.gl/pO2Oep (Last date accessed: 18 July 2017). 馮正一、梁家齊，2007。應用三維雷射掃描與GIS於邊坡穩定分析之研究。中華水土保持學報，38(3)，第243-258頁。 趙鍵哲、彭念豪，2005。以光達資料之控制直線求解單張像片外方位參數之模式探討與可行性評估。航測及遙測學刊，10(1)，第89-102頁。 蕭國鑫、劉進金、游明芳、曾義星，2005。航測與三維雷射掃瞄資料應用於九份二山地形變化分析。航測及遙測學刊，10(2)，第191-202頁。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67757 | - |
| dc.description.abstract | 於現代三維空間資訊測繪技術中，利用光達掃瞄技術所取得之高精度高密度的點雲資料受到各界領域廣泛應用。然而，受限於其資料獲取邏輯與機制，光達掃瞄所獲得的點雲資料時常有解析度不均、資料缺漏等問題。為此，本研究發展以影像與光達點雲為基礎之高細緻三維彩色點雲產製技術，利用光達資料內高精度的幾何資訊以及外部光學影像之輔助資訊以提升點雲成果品質。首先於影像的外方位參數求解上，應用影像匹配技術發展半自動之求解程序，提高資料整合作業效率。在完成影像對位程序後，利用攝影測量共線條件以及物空間中的直線與平面特徵，於幾何上重構點雲缺漏並密化原有點雲資料；於點雲顏色品質之改善上，萃取影像所包含豐富的光譜訊息賦予至已完成密化與重建後之點雲，產製高細緻的三維彩色點雲資料以提供後續應用。實驗成果顯示，本研究提出之方法能充分地融合點雲及影像資料，所取得彩色點雲成果於色彩或幾何完整性上皆有顯著提升，能夠有效降低現行自動化三維資料獲取技術實務應用之侷限，提升原始資料之實用效能與價值。 | zh_TW |
| dc.description.abstract | The Light Detection and Ranging (LiDAR) technique is a popular 3D surveying technique nowadays, which is capable of swiftly and precisely acquiring point cloud data with high density, for various engineering monitoring tasks. However, due to the limitation on the mechanism of the used technique, the point cloud data acquired by the LiDAR system frequently reaches unfavorable results, which have the flaws, such as uneven resolution and missing areas. In order to solve this problem, a combined approach for delivering high quality 3D colored point cloud is developed in this study, making the quality improvement of point cloud can be accomplished by integrating accurate geometric data from the LiDAR point cloud with the auxiliary information from images. The proposed approach, firstly determines the camera exterior orientation parameters by applying a semi-automatic process based on image matching technique. Secondly, for improving the geometric integrity of the raw data, the collinearity condition, as well as the geometry information from edge and planar features, will be used for point cloud reconstruction and densification. Finally, the abundant spectral information from multi-view images will be extracted and assigned to the refined point cloud data for generating high-quality result, which can be serve as a reliable and stable data source for further applications. The experiment result indicated that the proposed approach can adequately utilize the information from image data to increase the geometrical detail of the LiDAR point cloud. In conclusion, the quality improvement can be achieved through the use of data from different sources, which can successively reduces the limitation of current 3D surveying techniques, and also provides much more reliable and stable 3D information for applications. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:48:06Z (GMT). No. of bitstreams: 1 ntu-106-R03521119-1.pdf: 22323684 bytes, checksum: c4a2733449458a36e0ba084aae48e0e3 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員審定書 I
謝誌 II 摘要 III Abstract IV 目錄 V 圖目錄 VII 表目錄 X 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目的 3 1.3 論文架構 5 第二章 文獻回顧 6 2.1 整合異質料之三維空間資訊獲取 6 2.2 物像對應關係建立 7 2.2.1 光線交會法 7 2.2.2 整合式資料獲取系統 9 2.3 影像輔助空間資訊密化與重建 10 2.3.1 基於多像立體觀測之方法 10 2.3.2 基於單像光譜資訊之方法 12 2.4 多像色彩資訊整合 14 2.4.1 多張影像色差減緩 14 2.4.2 錯誤顏色資訊濾除 17 2.5 小結 21 第三章 研究方法 23 3.1 點雲與影像資料對應關係建立 24 3.1.1 基於影像坐標之三維物點追蹤 26 3.1.2 點雲彩色影像產製 28 3.1.3 共軛特徵偵測與匹配 30 3.1.4 迭代式影像外方位參數求解 34 3.2 點雲密化與重建推算 35 3.2.1 以直線特徵重建三維物點 35 3.2.2 平面特徵重建三維物點 39 3.3 多像色彩資訊整合 42 3.3.1 點雲色差改正 43 3.3.2 以單像物空間資訊進行錯誤顏色濾除 44 3.3.3 以多像光譜資訊進行錯誤顏色濾除 45 3.4 點雲資料庫建置 49 3.5 小結 50 第四章 實驗及成果分析 52 4.1 實驗配置 52 4.2 物像對應關係解算成果 54 4.3 點雲密化與重建成果 61 4.3.1 直線特徵重建與密化成果探討 62 4.3.2 平面特徵重建與密化成果探討 64 4.3.3 點雲重建與密化成果驗證 66 4.4 多像色彩資訊整合成果 70 4.4.1 點雲資料庫實作成果 70 4.4.2 點雲色差改正成果 71 4.4.3 基於物距之錯誤顏色濾除成果 75 4.4.4 基於多像色彩資訊之錯誤顏色濾除成果 80 4.5 小結 88 第五章 結論與建議 90 5.1 結論 90 5.2 建議 92 參考文獻 94 | |
| dc.language.iso | zh-TW | |
| dc.subject | 點雲上色 | zh_TW |
| dc.subject | 資料融合 | zh_TW |
| dc.subject | 光達掃瞄 | zh_TW |
| dc.subject | 數值攝影測量 | zh_TW |
| dc.subject | 三維點雲 | zh_TW |
| dc.subject | Digital Photogrammetry | en |
| dc.subject | Light Detection and Ranging (LiDAR) | en |
| dc.subject | Data Fusion | en |
| dc.subject | Point Cloud Coloring | en |
| dc.subject | 3D Point Clouds | en |
| dc.title | 融合光學影像與雷射掃瞄技術產製高細緻三維點雲 | zh_TW |
| dc.title | Integration of Digital Photogrammetry and Laser Scanning Technique for Generating High-Quality 3D Point Clouds | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊明德(Ming-Te Yang),張智安(Tee-Ann Teo),蔡富安(Fuan Tsai) | |
| dc.subject.keyword | 光達掃瞄,數值攝影測量,三維點雲,點雲上色,資料融合, | zh_TW |
| dc.subject.keyword | Light Detection and Ranging (LiDAR),Digital Photogrammetry,3D Point Clouds,Point Cloud Coloring,Data Fusion, | en |
| dc.relation.page | 101 | |
| dc.identifier.doi | 10.6342/NTU201702031 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-07-26 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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