Improved Multi-Hypothesis Test Point Cloud Noise Reduction for Neighborhood Drift
In 3D point-cloud data acquisition,the instrument,environment,algorithm,and other factors inevitably introduce noise into the data.The noise carried by point-cloud data affects the subsequent processing of the point cloud.To suppress the noise of 3D point-cloud data while preserving the different characteristics,a multi-hypothesis point cloud noise reduction method with an improved neighborhood drift is proposed.First,the neighborhood points and normal double-tensor voting method are used to describe the features.Subsequently,the transfer probability function is constructed using nonparametric estimation,and the weights of the new sampling points are calculated using the kernel regression method,whereby neighborhood drift is realized using a particle filter,and the nonlocal neighborhood is obtained after several iterations.The multiple normal directions of points with different features are then determined by the multi-hypothesis testing method,and the final normal directions are obtained by weighted average.Finally,feature and non-feature points are filtered using the multi-hypothesis testing method,and the point cloud model is denoised by generating multiple candidate points and using the objective function for optimization.To restore the noise model,the method described in this paper was used alongside Robust Implicit Moving Least Squares(RIMLS),Edge-Aware Resampling(EAR),L1,and PointNet methods.The error between the restored and original data was analyzed.The experimental results show that the average noise reduction accuracy obtained using the proposed method was 38.1％,41.3％,and 12.4％higher than the accuracies obtained using RIMLS,EAR,and L1,respectively.Compared with PointNet,the average noise reduction accuracy was lower by approximately 2.9％using the proposed method;however,database training and parameter adjustment are not required.
point cloud noise reductiontensor votingneighborhood driftnonparametric estimationkernel regressionmulti-hypothesis test
万方数据
维普
