Resampling to Speed Up Consolidation of Point Clouds

Abstract EN

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.