| DC 欄位 |
值 |
語言 |
| DC.contributor | 土木工程學系 | zh_TW |
| DC.creator | 郭志奕 | zh_TW |
| DC.creator | Chih-Yi ,KUO | en_US |
| dc.date.accessioned | 2005-7-17T07:39:07Z | |
| dc.date.available | 2005-7-17T07:39:07Z | |
| dc.date.issued | 2005 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=92322096 | |
| dc.contributor.department | 土木工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 數碼城市日益重要。而在數碼城市中，建物為其必要單元。光達資料(LIDAR data)的引進，為自動化建物重建之研究方向帶來可能性。
大比例尺向量圖具有精確之二維屋緣線，而光達資料具有豐富之屋頂面資訊。故本研究欲結合以上兩種資料之優勢，進行三維建物重建。本研究工作流程主要分為三部分：(1)資料整合、(2)建物頂共面分析、及(3)建物模塑。在資料整合部分，內容為兩資料之前處理，光達資料需去除地表起伏，而向量圖需建構封閉多邊型。在建物頂共面分析部分，以區塊成長法進行牆面和屋頂面之偵測。最後在建物模塑部分，內容為求取建物三維結構線段，並以整體平差進行建物幾何約制調整。
本研究並以台中大坑進行測試。光達資料點密度約 1.71(點/平方公尺)，向量圖比例尺為1:1000，重建完全正確率約90%，模塑誤差為0.17m。 | zh_TW |
| dc.description.abstract | Cyber city is getting important due to the developments of computer technology and the demands from city management. Building models, among others, could be the most important elements in a cyber city. Due to its maturity, LIDAR data has demonstrated profound potentials in fully automatic building reconstruction.
LIDAR data contains plenty of height information, while vector maps preserve accurate building boundaries. From the viewpoint of data fusion, we try to integrate LIDAR data and large-scale vector maps to perform building modeling. The proposed scheme comprises three major steps: (1) preprocessing of LIDAR data and vector maps, (2) segmentation and detection of wall faces and roof faces, and (3) building modeling. In the preprocessing of LIDAR data, the height variation of the above-ground objects is determined by subtracting the surface elevation from the terrain. The closed polygons for buildings are also obtained. In next stage, segmentation and detection of wall faces and roof faces is implemented by region growing. In the step of the building modeling, the construct edges of a building can be obtained. We also implement the geometric constraints by least squares adjustment.
The test data covers Tai-Chung city in the middle of Taiwan. The average density of LIDAR data is about 1.71 points per square meter. The vector maps are with a scale of 1:1,000. About 90% buildings are correctly reconstructed by the proposed method. The shaping error is about 0.17m. | en_US |
| DC.subject | 迪式三角網 | zh_TW |
| DC.subject | 三維建物重建 | zh_TW |
| DC.subject | 向量圖 | zh_TW |
| DC.subject | 光達資料 | zh_TW |
| DC.subject | Triangulated Irregular Network | en_US |
| DC.subject | Vector Maps | en_US |
| DC.subject | Building Reconstruction | en_US |
| DC.subject | LIDAR | en_US |
| DC.title | 結合光達資料與大比例尺向量圖重建三維建物模型 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Fusion of LIDAR Data and Large-Scale Vector Map for Building Reconstruction | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |