Performance evaluation of single and cross-dimensional feature detection and description

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.