利用移動式測繪系統獲得的街景影像，進行影像密匹配可以獲取房屋和道路表面的高密度三維點雲資料，建置高細節程度的三維都市模型，可供街景道路測繪、道路設施管理等各式應用。然而，與傳統的航空攝影測量進行比較，基於拍攝幾何上的差異，測繪車前拍影像會遇到與航空影像不同的困難，例如:前拍影像攝影方向與相機移動方向幾乎平行，而無法使用一般常用的核影像重建方法、基線航高(物距)比與航空影像相比過小，深度方向量測精度差、同一張影像的各像點成像比例尺差異大等問題。本文研究上述問題並使用四個方式來改善：1. 改變核影像糾正方法、2. 增加影像數量、3.增加前後時段影像基線長、4.增加不同角度相機拍攝之影像。
本文的實驗使用德國斯圖佳特大學研發的影像密匹配軟體SURE，採用測繪車前拍立體像對共22組影像，應用其多影像密匹配的特性，使用不同匹配組合產製點雲資料，並分析各組密匹配點雲品質，了解不同組合方式的改善程度。實驗測試成果顯示使用基於核線的核影像糾正法(Line-based rectification, LBR)在具有多張影像時可獲得較高的匹配成功率57.67%，優於相同組合使用基於平面的核影像糾正法(Plane-based rectification , PBR)的44.65%，而使用增加了傾斜45度相機拍攝之影像，雖然匹配成功率下降，但是其匹配點地面坐標精度0.103m，優於相同基線長前拍影像的0.199m。

High density 3D point clouds on the surfaces of buildings and roads can be obtained by dense matching of images taken forwards by cameras installed on a mobile mapping system (MMS) along streets. These point clouds can be used e.g. for reconstruction of 3D city models with high level of detail (LoD), and also for mapping of street scenery. However, due to the different photography geometry between MMS forward-taking image and traditional aerial image, there are some disadvantages for dense matching forward-taking images on MMS. For example, the photographic baseline is almost parallel with the photographing direction (PD). It causes that the general epipolar rectification method cannot be used. Since the base-height ratio is too small, the accuracy in depth direction is usually poor, and the variation of image scale for the points on the same image is large. This research aims at studying these problems and trying to solve them by: 1. changing the epipolar rectification methods, 2. increasing the number of matching pairs, 3. increasing baseline length of forward-taking images, 4. increasing the number of matching pairs with different PDs.
The experiments are done by using the image dense matching software SURE developed by the Stuttgart University, Germany. Also 22 image sets taken on a MMS are adopted for tests. Their interior and exterior orientation data are determined by photo triangulation. After generation of all sets of dense point clouds, their quality will be analyzed. The result shows that if we apply Line-based rectification (LBR) to generate the epipolar image, the multi-image sets will get a higher successful matching rate 57.67% than the one 44.65% determined with the same sets of images using plane-based rectification (PBR). If images tilted with 45 degrees are also adopted, the successful matching ratio decreases, but the accuracy of object coordinates of matched points increases to 0.103m which is better than the accuracy 0.199m determined with the forward-taking MMS image sets of the same baseline length.

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