Object detection and pose estimation from rectification of natural features using consumer RGB-D sensors - Lima, João Paulo Silva do Monte (2014)
Augmented Reality systems are able to perform real-time 3D registration of virtual and real objects, which consists in correctly positioning the virtual objects with respect to the real ones such that the virtual elements seem to be real. A very popular way to perform this registration is using video based object detection and tracking with planar fiducial markers. Another way of sensing the real world using video is by relying on natural features of the environment, which is more complex than using artificial planar markers. Nevertheless, natural feature detection and tracking is mandatory or desirable in some Augmented Reality application scenarios. Object detection and tracking from natural features can make use of a 3D model of the object which was obtained a priori. If such model is not available, it can be acquired using 3D reconstruction. In this case, an RGB-D sensor can be used, which has become in recent years a product of easy access to general users. It provides both a color image and a depth image of the scene and, besides being used for object modeling, it can also offer important cues for object detection and tracking in real-time. In this context, the work proposed in this document aims to investigate the use of consumer RGB-D sensors for object detection and pose estimation from natural features, with the purpose of using such techniques for developing Augmented Reality applications. Two methods based on depth-assisted rectification are proposed, which transform features extracted from the color image to a canonical view using depth data in order to obtain a representation invariant to rotation, scale and perspective distortions. While one method is suitable for textured objects, either planar or non-planar, the other method focuses on texture-less planar objects. Qualitative and quantitative evaluations of the proposed methods are performed, showing that they can obtain better results than some existing methods for object detection and pose estimation, especially when dealing with oblique poses.