ScholarWorks@성균관대학교

초록

Point cloud distance metrics play an essential role in assessing the quality of point clouds and computing losses in various point cloud processing tasks. The many metrics proposed to date have their own strengths and weaknesses, particularly in their sensitivity to structural or density imbalance and to outlier or point dispersion error traits. In this study, we presented an in-depth analysis of point cloud metrics, examined their sensitivities to density imbalance and point dispersion errors, and demonstrated how these sensitivities affect their performance in point cloud quality assessment and reconstruction. More significantly, we proposed a new metric, the Nested Chamfer Distance (NCD- n ), that allows adjusting its sensitivity to density imbalance and point dispersion error traits through the nested CD cycle, n . NCD- n achieves this adjustment by slicing out the one-to-one correspondence point pairs with bilaterally minimum distances, approximating Earth Mover's Distance (EMD) and allowing the fusion of EMD and CD's sensitivity properties. In addition, we analyzed the effect of shape-induced point density differences on distance metrics and proposed accounting for shape compensation in these metrics. Through experiments, we verified differences in metric sensitivities to error traits, the effectiveness of the proposed NCD- n in adjusting its sensitivity to density imbalance and point dispersion error traits, and the effects of sensitivity adjustment and shape-compensation on the performance of point cloud quality assessment and reconstruction tasks.

키워드