Camera Relocalization with Ellipsoidal Abstraction of Objects

International audienceWe are interested in AR applications which take place in man-made GPS-denied environments, as industrial or indoor scenes. In such environments, relocalization may fail due to repeated patterns and large changes in appearance which occur even for small changes in viewpoint. We investigate in this paper a new method for relocalization which operates at the level of objects and takes advantage of the impressive progress realized in object detection. Recent works have opened the way towards object oriented reconstruction from elliptic approximation of objects detected in images. We go one step further and propose a new method for pose computation based on ellipse/ellipsoid correspondences. We consider in this paper the practical common case where an initial guess of the rotation matrix of the pose is known, for instance with an inertial sensor or from the estimation of orthogonal vanishing points. Our contributions are twofold: we prove that a closed-form estimate of the translation can be computed from one ellipse-ellipsoid correspondence. The accuracy of the method is assessed on the LINEMOD database using only one correspondence. Second, we prove the effectiveness of the method on real scenes from a set of object detections generated by YOLO. A robust framework that is able to choose the best set of hypotheses is proposed and is based on an appropriate estimation of the reprojection error of ellipsoids. Globally, considering pose at the level of object allows us to avoid common failures due to repeated structures. In addition, due to the small combinatory induced by object correspondences, our method is well suited to fast rough localization even in large environments

Camera Pose Estimation with Semantic 3D Model

International audienceIn computer vision, estimating camera pose from correspondences between 3D geometric entities and their projections into the image is a widely investigated problem. Although most state-of-the-art methods exploit simple primitives such as points or lines, and thus require dense scene models, the emergence of very effective CNN-based object detectors in the recent years have paved the way to the use of much lighter 3D models composed solely of a few semantically relevant features. In that context, we propose a novel model-based camera pose estimation method in which the scene is modeled by a set of virtual ellipsoids. We show that 6-DoF camera pose can be determined by optimizing only the three orientation parameters, and that at least two correspondences between 3D ellipsoids and their 2D projections are necessary in practice. We validate the approach on both simulated and real environments

Camera Relocalization with Ellipsoidal Abstraction of Objects

International audienceWe are interested in AR applications which take place in man-made GPS-denied environments, as industrial or indoor scenes. In such environments, relocalization may fail due to repeated patterns and large changes in appearance which occur even for small changes in viewpoint. We investigate in this paper a new method for relocalization which operates at the level of objects and takes advantage of the impressive progress realized in object detection. Recent works have opened the way towards object oriented reconstruction from elliptic approximation of objects detected in images. We go one step further and propose a new method for pose computation based on ellipse/ellipsoid correspondences. We consider in this paper the practical common case where an initial guess of the rotation matrix of the pose is known, for instance with an inertial sensor or from the estimation of orthogonal vanishing points. Our contributions are twofold: we prove that a closed-form estimate of the translation can be computed from one ellipse-ellipsoid correspondence. The accuracy of the method is assessed on the LINEMOD database using only one correspondence. Second, we prove the effectiveness of the method on real scenes from a set of object detections generated by YOLO. A robust framework that is able to choose the best set of hypotheses is proposed and is based on an appropriate estimation of the reprojection error of ellipsoids. Globally, considering pose at the level of object allows us to avoid common failures due to repeated structures. In addition, due to the small combinatory induced by object correspondences, our method is well suited to fast rough localization even in large environments

Camera Pose Estimation with Semantic 3D Model

International audienceIn computer vision, estimating camera pose from correspondences between 3D geometric entities and their projections into the image is a widely investigated problem. Although most state-of-the-art methods exploit simple primitives such as points or lines, and thus require dense scene models, the emergence of very effective CNN-based object detectors in the recent years has paved the way to the use of much lighter 3D models composed solely of a few semantically relevant features. In that context, we propose a novel model-based camera pose estimation method in which the scene is modeled by a set of virtual ellipsoids. We show that 6-DoF camera pose can be determined by optimizing only the three orientation parameters, and that at least two correspondences between 3D ellipsoids and their 2D projections are necessary in practice. We validate the approach on both simulated and real environments