Point clouds acquired by LiDAR typically contain a large number of outliers, which can substantially degrade downstream processing. Existing outlier removal methods suffer from significant performance degradation on point clouds with high outlier ratios. In this study, we propose a multi-stage outlier removal method called MORPH for point clouds with high outlier ratios. The proposed method first employs local reachability density combined with density-peak clustering to coarsely eliminate prominent outliers while preserving the global structural skeleton. Subsequently, a structure-adaptive refinement stage is introduced to generate incomplete point clouds and candidate points. Finally, a completion strategy that integrates pairwise scale consistency with local plane residual score is proposed to identify inlier points from the candidate points. Outlier removal is completed by using the obtained inlier points to fill the incomplete point clouds. Experimental results on synthetic high-contamination point clouds and real-world scanned point clouds demonstrate that MORPH achieves robust outlier removal performance while better preserving valid geometric structures.
Meng et al. (Wed,) studied this question.