Zhang, G and Karasev, P and Brilakis, I and Vela, PA (2012) Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data. In: the 2012 ASCE International Workshop on Computing in Civil Engineering, 17-6-2012 to 20-6-2012, Clearwater Beach, FL, United States of America pp. 317-324..Full text not available from this repository.
Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Depositing User:||Cron Job|
|Date Deposited:||26 Aug 2012 16:10|
|Last Modified:||04 Feb 2013 01:54|
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