Toward a smart camera for fast high-level structure extraction

This paper presents an initial framework to extract high-level structures from man-made environments, by means of a novel methodology that combines stereo vision, binary descriptors and parallel processing implemented on a GPU. High-level structures such as planes, spheres and cubes provide vital information of the world, essential to perform applications in the field of robotics, augmented reality and computer vision. However, their extraction involves several computational challenges, especially because their application context requires solving real-time and environment operation constraints. Hence, stereo vision-based attempts have been proposed, without achieving real-time performance because they require a rectification stage running in the frame-to-frame basis, increasing the computational burden. Therefore, in contrast to typical stereo algorithms, the proposed methodology is developed on the basis of a semi-calibrated stereo rig, which means that rectification stage is avoiding, thus enabling to invest computational cost in critical stages and consequently achieving a frame rate up to 50 fps for the whole process.