A framework of three-dimensional object recognition which needs only a few reference images

We propose a new framework for recognizing three-dimensional (3D) objects that needs few reference images. We tackle a key issue of 3D object recognition; the trade-off between the number of reference images and recognition accuracy. We assume that a reference image is a photo of the object from an arbitrary position, typical of those used to exhibit goods on the web. The framework, first, estimates a 3D model of the object from a video sequence (the input), and generates a single target image by spherical projection of the 3D model and texture. The framework recognizes the object by matching the target image against the reference images stored in the database. The descriptor matching approach is used, with the image getting the most votes becoming the recognition result. The vote score is computed from the vector distance between key-points, not only for each given key-point but also for key-points in its vicinity.