Kinect获取植物三维点云数据的去噪方法

Denoising Method of 3-D Point Cloud Data of Plants Obtained by Kinect

为解决Kinect获取的玉米三维点云数据噪声影响三维重建精度的问题,根据Kinect所获取的点云数据特点,采用多帧数据融合的方法获取更完整的三维点云数据并对点云数据进行初步平滑;通过对Kinect所获数据噪声进行分析,提出了一种基于密度分析和深度数据双边滤波的方法,分别对离群点噪声和内部高频噪声进行处理。以Kinect获取的玉米及茄子的三维点云数据进行去噪实验,所用去噪时间仅为传统双边滤波去噪时间的2.71%和1.78%,并且能够达到很好的去噪效果。结果表明,所提方法能够方便、快捷地去除不同尺度的噪声,同时保留边缘数据的完整性,获得良好的植物三维点云数据。

In order to solve the difficult acquisition of plants＇ 3-D point cloud data,the Kinect was adopted to collect the 3-D point cloud data of corn. Compared with the usual 3-D scanning equipment,Kinect can rapidly and efficiently acquire the data with lower cost. But the accuracy of data acquired by Kinect is low. It is valuable to denoise the data. According to the characteristics of the point cloud data acquired by Kinect,the data were pre-processed and smoothed. In this paper,a multi frame data fusion method was used to obtain more complete plant 3-D point cloud data,and it played a role in smoothing.A denoising algorithm based on density analysis and depth data bilateral filtering methods were proposed to process the outlier noise and internal high-frequency noise. In the experiment of corn and eggplant internal high-frequency noise denoising,compared with the traditional bilateral filtering,the denoising time of the algorithm in this paper was only 2. 71% and 1. 78% of traditional bilateral filtering and the noise was well removed by adjusting the parameters. The experimental results show that the proposed method can easily and quickly remove the noise of different scales,while preserving the integrity of edge data. Consequently,the good 3-D point cloud data of the plant could be obtained.