On the development of a robust, fast and lightweight keypoint descriptor

In this paper we introduce BRAND—Binary Robust Appearance and Normal Descriptor, a novel descriptor which efficiently combines appearance and geometric information from RGB-D images, that is largely invariant to rotation and scale transformations. Based on relevant characteristics of successful image only descriptors, we define a set of eight fundamental requirements to guide the design and evaluation of descriptors that also use depth information. We then describe the design of BRAND, followed by the evaluation of its performance according to those requirements. We also show how BRAND can be simplified in order to obtain a higher performance version, that we named BASE, for applications that require speed performance, but do not demand rigorous scale and rotation invariance.We compare the performance of BRAND against three standard descriptors on real world data. Results of several experiments demonstrate that as far as precision and robustness is concerned, BRAND compares favorably to SIFT and SURF for textured images, and to Spin-Image, for geometrical shape information. Furthermore, BRAND attains improved results when compared to state of the art descriptors that are based either on texture or geometry alone, or on their combination.Finally, we report on the use of BRAND in two applications for which we show that it provides reliable results for the registration of indoor textured depth maps and for object recognition in tasks that require the extraction of semantic knowledge.