Research And Implement On The Algorithm Of Point Cloud Denoising And Simplification

Due to recent advances in 3D-measurement and computer technology,real object boundary surfaces are acquired by many means,and they can be disposed,processed, analyzed and be applied.The point clouds adopted by measurement are studied and used commonly,and an exciting domain,3-D digital geometry process is gestated.Based on the review and analysis of existing research achievements,the paper proposes the methods for the denoising and simplification on the point cloud.The main research and achievements as follows:The paper presents an improved bilateral filter for point cloud denoising based on self-adaptive neighbors.Firstly,the bizarre noise is delete based on self-adaptive neighbors, then we improved the bilateral filter on robust and feature-preservation,and the self-adaptive k-neighbor improved the method efficiency.For more,we evaluate the improved operator by method noise,and visualized result is given at last.The paper presents a curvature self-adaptive mixed method based on geometry image. The method combined the advantages of curvature sample and random sample,and realized the strategy both on distance and curvature rule.We achieved a good compromise in efficiency and result,and the method can be used in application.All of the algorithms presented above have been implemented in a prototypical system and tested with data obtained from objects in real world.Results of these examples indicate the correctness of the algorithms in this paper.