期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 6, 页码 4121-4145出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2901539
关键词
airborne laser scanning (ALS); building abstraction; building contour decomposition; building contours; building regularization; large-scale airborne point clouds; LoD1 building models
类别
资金
- National Natural Science Foundation of China [41301521, 31770591]
- China Postdoctoral Science Foundation [2016M601823]
This paper introduces a novel methodology for residential building contouring from large-scale airborne point clouds. Unlike other methods that handle linearization and regularization of the linear primitives separately by imposing rigid constraints, we propose an optimization-based linearization and global regularization to form accurate, topologically error-free, and lightweight polygons. To this end, we enhance the classic density-based spatial clustering of applications with noise algorithm to segment individual building entities at the instance level. The initial contours of each individual building are then delineated and further decomposed by a novel topologically aware propagation process and a global optimization technique. The decomposed linear primitives are fed into the global regularization step, from which the regular shapes are learned and enforced hierarchically by imposing constraints, such as parallelism, homogeneity, orthogonality, and collinearity. Based on the concept of hybrid representation, the regularized and unaltered linear primitives are jointly connected in an esthetic way. Various experiments using representative buildings and large-scale residential scenes from the Dutch AHN3 data set have shown that the proposed methodology generates meaningful building contouring representation in terms of accuracy, compactness, topology, and levels of detail abstraction while being robust and scalable.
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