BAG: A Binary Descriptor for RGB-D Images Combining Appearance and Geometric Cues.

Feature matching forms the basis for numerous computer vision applications. With the rapid development of 3D sensors, the availability of RGB-D images has been increased stably. Compared to traditional 2D images, the additional depth images in RGB-D images can provide more geometric information. In this paper, we propose a new efficient binary descriptor (namely BAG) for RGB-D image representation by combining appearance and geometric cues. Experimental results show that the proposed BAG descriptor produces better feature matching performance with faster matching speed and less memory than the existing methods.