Three-dimensional (3D) local feature detection and description techniques are widely used for object registration and recognition applications. Although several evaluations of 3D local feature detection and description methods have already been published, these are constrained in a single dimensional scheme, i.e. either 3D or 2D methods that are applied onto multiple projections of the 3D data. However, cross-dimensional (mixed 2D and 3D) feature detection and description are yet to be investigated. Here, the authors evaluated the performance of both single and cross-dimensional feature detection and description methods on several 3D data sets and demonstrated the superiority of cross-dimensional over single-dimensional schemes.

中文翻译：

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一维和多维特征检测和描述的性能评估

三维（3D）局部特征检测和描述技术已广泛用于对象注册和识别应用程序。尽管已经发布了3D局部特征检测和描述方法的几种评估方法，但这些评估方法仅限于一维方案，即应用于3D数据多个投影的3D或2D方法。但是，跨维度（2D和3D混合）特征检测和描述尚待研究。在这里，作者评估了在几个3D数据集上的单维和跨维特征检测和描述方法的性能，并证明了跨维优于单维方案。