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In this paper, we propose an object-based camera pose estimation from asingle RGB image and a pre-built map of objects, represented with ellipsoidalmodels. We show that contrary to point correspondences, the definition of acost function characterizing the projection of a 3D object onto a 2D objectdetection is not straightforward. We develop an ellipse-ellipse cost based onlevel sets sampling, demonstrate its nice properties for handling partiallyvisible objects and compare its performance with other common metrics. Finally,we show that the use of a predictive uncertainty on the detected ellipsesallows a fair weighting of the contribution of the correspondences whichimproves the computed pose. The code is released athttps://gitlab.inria.fr/tangram/level-set-based-camera-pose-estimation.

Level Set-Based Camera Pose Estimation From Multiple 2D/3D Ellipse-Ellipsoid Correspondences