KDD: A kernel density based descriptor for 3D point clouds
作者：
Highlights：
• A novel 3D local descriptor (KDD) which achieves a satisfactory and balanced performance in terms of descriptiveness, robustness, and compactness, is proposed; furthermore, the proposed KDD is very simple since it encodes the spatial distribution of points, avoiding computing any geometric attributes and needing no rotational projection operations.
• The KDD is combined with different matching metrics for different datasets and the strategy for selecting different matching metrics for datasets with diverse levels of resolution qualities is provided.
• We apply the proposed method on a real-world dataset, i.e. the Terracotta fragment models, and the favorable results demonstrate the effectiveness of KDD and highlight the utility of the proposed method.
摘要
•A novel 3D local descriptor (KDD) which achieves a satisfactory and balanced performance in terms of descriptiveness, robustness, and compactness, is proposed; furthermore, the proposed KDD is very simple since it encodes the spatial distribution of points, avoiding computing any geometric attributes and needing no rotational projection operations.•The KDD is combined with different matching metrics for different datasets and the strategy for selecting different matching metrics for datasets with diverse levels of resolution qualities is provided.•We apply the proposed method on a real-world dataset, i.e. the Terracotta fragment models, and the favorable results demonstrate the effectiveness of KDD and highlight the utility of the proposed method.
论文关键词：3D feature descriptor,Kernel density estimation,Point cloud registration,KL divergence
论文评审过程：Received 4 February 2020, Revised 8 August 2020, Accepted 7 October 2020, Available online 8 October 2020, Version of Record 15 October 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107691
