| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 103 === The point cloud segmentation has been a significant progress to point cloud classification and ground object reconstruction. In addition, the result of segmentation has directly influence over the following analysis and utilization. Considering that LiDAR (light detection and ranging) scanners are attributes of blind systems, the object-based concept is used to analyze point clouds from large amounts of discrete data to point cloud objects, which are composed of parent-child relationships. The methods of point cloud segmentation are diverse in accordance with purposes and demands. For instance, a model-driven approach, RANSAC (random sample consensus), which is robust and efficient, is used to building extraction and reconstruction. Moreover, a data-driven approach, clustering, which clusters highly correlated points into objects, is applied to irregular object identification and classification by calculating Euclidean distance between points.
The study is essentially built on the object-based point cloud analysis (OBPCA) and proposes a suitable segmentation method to point clouds. Since the features, also known as attributes, are considered in the object-based point cloud analysis, they are not only beneficial to object analysis, but also provide heterogeneities to the progress of segmentation. The heterogeneity is exploited to simplify the procedure, to improve the efficiency of point cloud segmentation, and to adapt different point cloud distributions of scenes. Therefore, in this research, current methods of segmentation are consolidated and interpreted, and a multi-scale segmentation algorithm is developed for increasing operational efficiency without reducing overall accuracy of classification.
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