Application of DBSCAN clustering and improved bilateral filtering algorithm in point cloud denoising

doi:10.13474/j.cnki.11-2246.2019.0358

Abstract

Abstract: The density-based DBSCAN clustering algorithm is used to denoise the point cloud data, and the smoothing effect is achieved by the improved bilateral filtering method that conducts smooth treatment. Finally not only the noise points are effectively removed, but also characteristics of the point cloud model are retained. This article uses the representative building of Shenyang during the Republic of China-Shenyang Financial Museum as the experimental model. The experimental results show that the point cloud data obtained by the DBSCAN clustering algorithm and the improved bilateral filtering process are far more accurate than the original point cloud data, and the data is more accurate and denoising, point cloud denoising is better.

Key words: DBSCAN clustering algorithm, Bilateral filtering method, Noise point, point cloud, density

CLC Number:

P237

QU Jinbo, WANG Yan, ZHAO Qi. Application of DBSCAN clustering and improved bilateral filtering algorithm in point cloud denoising[J]. Bulletin of Surveying and Mapping, 2019, 0(11): 89-92.

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