5 accepted papers
In this paper, we propose an object-based camera pose estimation from a single RGB image and a pre-built map of objects, represented with ellipsoidal models. We show that contrary to point correspondences, the definition of a cost function characterizing the projection of a 3D object onto a 2D objec…
Recent years have seen the emergence of very effective ConvNet-based object detectors that have reconfigured the computer vision landscape. As a consequence, new approaches that propose object-based reasoning to solve traditional problems, such as camera pose estimation, have appeared. In particular
In 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 emer…
We show that, in images of man-made environments, the horizon line can usually be hypothesized based on an a contrario detection of second-order grouping events. This allows constraining the extraction of the horizontal vanishing points on that line, thus reducing false detections. Experiments made…
We consider the problem of camera pose estimation from a scene model obtained beforehand by a structure-from-motion (SfM) algorithm. The model is made of 3D points, each one of them being represented by its coordinates and a set of photometric descriptors such as SIFT, extracted from some of the inp…