- 學位論文
多尺度點雲物件分割之研究
A Study on Multi-Scale Object-Based Point Cloud Segmentation
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摘要
光達點雲分割為點雲分類及地物建模的重要步驟，分割的成果直接影響後續點雲分析及應用。基於光達掃描為盲系統，因此如何自大量且離散的資料中，分割出具有從屬關係的點雲，即是以物件為基礎的概念分析點雲。點雲分割的方法隨應用目的不同而設計，常用的有以模型為導向的隨機樣本一致演算法，具有強鈍性及高效率的特性，主要用以建物的萃取與重建；對於不規則物體的辨識及分類，則以資料為導向的群聚法，是以點雲資料間的歐幾里得距離為基準，利用鄰近點在幾何上的高關聯性，群聚符合群聚門檻的點位。 本研究將建構在以物件為基礎的點雲分析上，提出合適的點雲分割方法。由於以物件為基礎的點雲分析，額外考量點雲物件的特徵，不僅利於點雲物件的分析，更作為點雲分割的重要依據，藉由異質性指標引入，可以簡化點雲分割的流程，並提升點雲分割的效率，同時，亦能適應不同場景及點雲分佈的資料。有鑑於此，本研究分析並綜合現有的光達點雲分割方法，發展多尺度點雲分割的演算法，適合於點雲物件分類，在不影響分類精度的前提下，提升整體運算的效率。
並列摘要
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.
參考文獻
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