雷射掃描點雲資料結構化作業分析、幾何品質評估及應用領域之研究(I)
Date Issued
2005
Date
2005
Author(s)
DOI
932211E002054
Abstract
The authors investigated in this study by exploiting the concepts of structuralization on
LIDAR point cloud and registering laser scanning data sets from different stations. The former
task conducted by two streams includes extracting planes as well as line features and point
features derivation. One of the two methods is to table the LIDAR point cloud attributed with
geometric and topologic relationship of the points within near neighborhood and thus be able to
classify the point cloud into planar features. Line features and point features are then derived
seeking neighboring planes. The other method is to employ 3D grid structure well addressing
point cloud into 3-D topology and followed by (a). region growing for hypothesizing planes and
(b). iterated Hough Transform for refining the plane-features. Line features and point features can
then be derived based on the previous solution on plane extraction. The latter task is mainly
focused on developing the algorithms in which 3D line feature correspondences for registering
multiple data sets with overlapping scene can be established by using geometric constraints in an
automatic fashion. The spatial similarity transformation can then be performed by matched 3D
line feature correspondence.
Subjects
Structuralization
Region Growing
Iterated Hough Transform
3D Line Features
Spatial Similarity Transformation
Publisher
臺北市：國立臺灣大學土木工程學系暨研究所
Type
report
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