Abstract 3D feature description is one of the central techniques that rely on point clouds since a lot of point cloud processing techniques apply the point-to-point correspondences that are achieved via feature descriptors as input data. The feature descriptor encodes the information of the underlying surface around the feature point so as to make a local surface distinguished from another. The focus of the existing descriptors is accumulating the geometric or topological measurements into histograms or encoding the 2D images that are acquired by rotationally projecting the 3D local surfaces onto 2D planes. Histograms can hardly deal with three or more dimensional information, and the rotational projection operation does bring much unnecessary intermediate computations. To overcome these limitations, in this article, a descriptor named Kernel Density Descriptor (KDD) has been presented. One core contribution of this method is to encode the information of the whole 3D space around the feature point via kernel density estimation, and another is providing the strategy for selecting different matching metrics for datasets with diverse levels of resolution qualities. We compare KDD against several representative descriptors on publicly available datasets, the experimental results demonstrate that the KDD descriptor achieves a satisfactory and balanced performance in terms of descriptiveness, robustness, and compactness, furthermore, the comparisons validate the overall superiority of our method. The benefits and applicability on object registration and recognition and 3D object reconstruction are demonstrated by the favorable results that are obtained for both public datastes and the real-world point clouds of Terracotta fragments.

中文翻译：

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KDD：基于核密度的 3D 点云描述符

摘要 3D 特征描述是依赖点云的核心技术之一，因为许多点云处理技术应用通过特征描述符实现的点对点对应作为输入数据。特征描述子对特征点周围的下层表面信息进行编码，以区分局部表面。现有描述符的重点是将几何或拓扑测量累积成直方图或编码通过将 3D 局部表面旋转投影到 2D 平面上获得的 2D 图像。直方图很难处理3维以上的信息，旋转投影操作确实带来了很多不必要的中间计算。为了克服这些限制，在本文中，已经提出了一个名为核密度描述符 (KDD) 的描述符。该方法的一个核心贡献是通过核密度估计对特征点周围的整个 3D 空间的信息进行编码，另一个是提供为具有不同分辨率质量级别的数据集选择不同匹配度量的策略。我们将 KDD 与公开可用数据集上的几个代表性描述符进行比较，实验结果表明 KDD 描述符在描述性、鲁棒性和紧凑性方面取得了令人满意和平衡的性能，此外，这些比较验证了我们方法的整体优越性。