基于噪声分类的双边滤波点云去噪算法

针对三维点云数据模型在去噪光顺中存在不同尺度噪声的问题,提出一种基于噪声分类的双边滤波点云去噪算法。该算法首先将噪声细分为大尺度和小尺度噪声,并使用统计滤波结合半径滤波对大尺度噪声进行去除;然后对三维点云数据进行曲率估计,并对现有点云双边滤波进行改进,增强其鲁棒性和保特征性;最后使用改进的双边滤波对小尺度噪声进行光顺,实现三维点云数据模型的去噪、光顺。与单独使用双边滤波、Fleishman双边滤波相比,改进算法在三维点云数据模型光顺平均误差指标上分别降低了50.53%和21.67%。实验结果表明,该改进算法对噪声进行尺度的细分既提高了计算效率,又避免了过光顺和细节失真,较好地保持模型中的几何特征。

Denoising algorithm for bilateral filtered point cloud based on noise classification

Focusing on the issue that different scale noise exists in denoising and smoothing of 3D point cloud data model, a bilateral filtering denoising algorithm for 3D point cloud based on noise classification was proposed. Firstly, the noise points were subdivided into the large-scale and the small-scale noise, and the large-scale noise was removed with statistical filtering and radius filtering. Secondly, the curvature of the 3D point cloud data was estimated, and the bilateral filter was improved to enhance the robustness and security. Finally, the small-scale noise was smoothed with the improved bilateral filter to achieve the smoothing and denoising of 3D point clouds. Compared with the algorithms simply based on bilateral filtering or Fleishman bilateral filtering, the smoothing average error index of 3D point cloud data model obtained by the proposed method respectively decreased by 50.53% and 21.67%. The experimental results show that the proposed algorithm increases the efficiency of calculation by scale subdivion of noise points, and avoids excessive smoothing and detail distortion, which can better maintain the geometric characteristics of the model.