计算机工程与应用2016，Vol.52Issue(20)：188-192,5.DOI:10.3778/j.issn.1002-8331.1603-0354
点云模型的噪声分类去噪算法
Noise classification denoising algorithm for point cloud model
摘要
Abstract
Aiming at the problems that different scale noise exists in denoising and smoothing of 3D point cloud data and time consuming of algorithm, the denoising algorithm for point cloud data based on noise classification is proposed. According to the distribution characteristics, the noise points are divided into large-scale and small-scale noise. Firstly, the large-scale noise is removed by statistical filtering and radius filtering. Then the small-scale noise is smoothed with fast bilateral filtering. Finally, the purpose of denoising and rapid smoothing for 3D point cloud data are achieved. Compared with the traditional bilateral filtering, the computing efficiency is improved using fast bilateral filtering to smooth the point cloud data. The experimental results show that the proposed algorithm can fast denoise and smooth for 3D point cloud data, which can effectively maintain the geometric features of the scanned object.关键词
点云去噪/快速双边滤波/统计滤波/条件滤波/平滑Key words
point cloud denoising/fast bilateral filtering/statistical filtering/radius filtering/smoothing分类
信息技术与安全科学引用本文复制引用
李鹏飞,吴海娥,景军锋,李仁忠..点云模型的噪声分类去噪算法[J].计算机工程与应用,2016,52(20):188-192,5.基金项目
国家自然科学基金（No.61301276）；陕西省工业科技攻关项目（No.2015GY034）；西安工程大学学科建设经费资助（No.107090811）。 （）
