近年來，光達技術已經成為快速獲取三維點雲資料的一種重要方法。本文以Tensor Voting演算法為基礎，初步嘗試由光達點雲資料中自動地萃取出地形結構線。本文針對影響特徵萃取的兩個因子：搜尋半徑和控制張量傳遞衰減程度的尺度因子詳細地討論，以分析它們對地形結構線萃取的影響。
為了求出地形結構線的位置，本研究以點雲的線特徵強度進行分析。將線特徵較強的點雲分塊，並求出每分塊內所有點雲坐標之加權平均後，將每個經由加權平均所獲得之坐標加以連線，以展現出地形結構線。實驗結果顯示Tensor Voting演算法在特徵資訊之獲取上有良好之成果。其中以加大搜尋半徑和一定限度的衰減尺度因子對於特徵萃取有較大正面的助益。

The LiDAR technology has become an important　method to quickly　get 3-D point clouds during the recent years. This study　attempts to automatically　extract structure lines from　LiDAR point clouds based on tensor　voting algorithm.　Also, this study analyzes the effect of the radius of　the search window and the scale factor, which controls the degree　of attenuation　during tensor communication, on the　results.
In order to acquire the location of the structure lines, the　linear feature　strength of point clouds is used. First,　points with higher values are divided by 3D blocks, and then the weighted average coordinates in every block are calculated. Finally, we connect these points to derive the structure lines. The result shows that the tensor voting algorithm has good performance on acquiring feature information. Besides, enlarging the radius of search window and the scale factor (in a limited value) is also positive to feature extraction.

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