Hierarchical denoising method of crop 3D point cloud based on multi-view image reconstruction

Abstract

Since the advantages of low cost and high efficiency, the three dimensional point cloud reconstruction based on multi-view image sequence and stereo matching has been widely used in agriculture. However, the reconstructed three dimensional point cloud often contains a lot of noise data because of the complex morphology of crop. In order to improve the precision of three dimensional point cloud reconstruction, the paper proposed a hierarchical denoising method which first adopts the density clustering to deal with the large scale outliers, combined with crop morphology analysis, and then smooths the small scale noise with fast bilateral filtering. Two crops of rice and cucumber were taken to validate the method in the experiments. The results demonstrated that the proposed method can achieve better denoising results while preserving the integrity of the boundary of crop 3D model.