Abstract
Processing of large-scale scattered point clouds has currently become a hot topic in the field of computer graphics research. A supposedly valid tool in producing a set of denoised, outlier-free, and evenly distributed particles over the original point clouds, Weighted Locally Optimal Projection (WLOP) algorithm, has been used in the consolidation of unorganized 3D point clouds by many researchers. However, the algorithm is considered relatively ineffective, due to the large amount of the point clouds data and the iteration calculation. In this paper, a resampling method applied to the point set of 3D model, which significantly improves the computing speed of the WLOP algorithm. In order to measure the impact of error, which will increase with the improvement of calculation efficiency, on the accuracy of the algorithm, we define two quantitative indicators, that is, the projection error and uniformity of distribution. The performance of our method will be evaluated by using both quantitative and qualitative analyses. Our experimental validation demonstrates that this method greatly improves calculating efficiency, notwithstanding the slightly reduced projection accuracy in comparison to WLOP.
Highlights
As a popular topic of growing interest in the fundamental research of computer graphics, reverse engineering in reconstructing 3D models from unorganized point cloud data has been considered by many authors and various results on surface reconstruction from point clouds have been published in recent years
We briefly review the application and research of Weighted Locally Optimal Projection (WLOP) algorithm among the various methods hitherto developed for the processing of point clouds
In this paper we introduce an improved algorithm for consolidation of point clouds
Summary
As a popular topic of growing interest in the fundamental research of computer graphics, reverse engineering in reconstructing 3D models from unorganized point cloud data has been considered by many authors and various results on surface reconstruction from point clouds have been published in recent years. WLOP (Weighted Locally Optimal Projection) operator [15] and LOP (Locally Optimal Projection) operator [14] have proven better immunity against noise and outlier of raw scanned data, in addition to their advantage in creating evenly redistributed point clouds. These methods are found limited in preserving geometrical features of the model in the projection (see [16, 17]). The open problem we intend to solve in Mathematical Problems in Engineering this paper is the slow computing speed caused by multiple iterations in previously proposed models and limitation in results obtained from reconstructing large point set data due to the mathematical complexity
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