Binary local descriptors are widely used in computer vision thanks to their compactness and robustness to many image transformations such as rotations or scale changes. However, more complex transformations, like changes in camera viewpoint, are difficult to deal with using conventional features due to the lack of geometric information about the scene. In this paper, we propose a local binary descriptor which assumes that geometric information is available as a depth map. It employs a local parametrization of the scene surface, obtained through depth information, which is used to build a BRISK-like sampling pattern intrinsic to the scene surface. Although we illustrate the proposed method using the BRISK architecture, the obtained parametrization is rather general and could be embedded into other binary descriptors. Our simulations on a set of synthetically generated scenes show that the proposed descriptor is significantly more stable and distinctive than popular BRISK descriptors under a wide range of viewpoint angle changes.
The increasing availability of texture+depth (RGBD) content has recently motivated research towards the design of image features able to employ the additional geometrical information provided by depth. Indeed, such features are supposed to provide higher robustness than conventional 2D features in presence of large changes of camera viewpoint. In this paper we consider the first stage of RGBD image matching, i.e., keypoint detection. In order to obtain viewpoint-covariant keypoints, we design a filtering process, which approximates a diffusion process along the surfaces of the scene, by means of the information provided by depth. Next, we employ this multiscale representation to find keypoints through a multiscale keypoint detector. The keypoints obtained by the proposed detector provide substantially higher stability to viewpoint changes than alternative 2D and RGBD feature extraction approaches, both in terms of repeatability and image classification accuracy. Furthermore, the proposed detector can be efficiently implemented on a GPU.
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