An Effective Denoising Method for the Point Cloud of Trees Based on the Hybrid Filtering Scheme

This is the authors' abstract. We don't add key points for this paper.

The point cloud-based 3D reconstruction techniques have been widely used in the tree phenotype monitoring, and the data denoising is the essential step due to the various noise problems encountered in practical applications. An effective and accurate denoising method for the point cloud of trees by the hybrid filtering scheme is proposed in this paper. The statistical filtering algorithm is firstly well studied to give the best parameters to remove the outliers of the point cloud data; and then an improved voxel filtering method is proposed by replacing the representative points of a voxel with the neighboring points of the center of gravity that is computed from the voxel in the original point cloud. By comparing the time cost and the spatial features to maintain, the experimental results fully prove the proposed method can effectively reduce the multiscale noise without damaging the geometric structure of the source data, performing better than the traditional voxel filter and the statistical filter.