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A Framework for Automatic Modeling from Point Cloud Data
Nov. 2013 (vol. 35 no. 11)
pp. 2563-2575
C. Poullis, Immersive & Creative Technol. Lab., Cyprus Univ. of Technol., Limassol, Cyprus
We propose a complete framework for the automatic modeling from point cloud data. Initially, the point cloud data are preprocessed into manageable datasets, which are then separated into clusters using a novel two-step, unsupervised clustering algorithm. The boundaries extracted for each cluster are then simplified and refined using a fast energy minimization process. Finally, three-dimensional models are generated based on the roof outlines. The proposed framework has been extensively tested, and the results are reported.
Index Terms:
Data models,Three-dimensional displays,Vectors,Solid modeling,Surface treatment,Clustering algorithms,Covariance matrices,shape refinement,Three-dimensional reconstruction,3D modeling,point cloud,clustering,segmentation
Citation:
C. Poullis, "A Framework for Automatic Modeling from Point Cloud Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2563-2575, Nov. 2013, doi:10.1109/TPAMI.2013.64
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