Pose Priors for Simultaneously Solving Alignment and Correspondence

Abstract. Estimating a camera pose given a set of 3D-object and 2Dimage feature points is a well understood problem when correspondences are given. However, when such correspondences cannot be established a priori, one must simultaneously compute them along with the pose. Most current approaches to solving this problem are too computationally intensive to be practical. An interesting exception is the SoftPosit algorithm, that looks for the solution as the minimum of a suitable objective function. It is arguably one of the best algorithms but its iterative nature means it can fail in the presence of clutter, occlusions, or repetitive patterns. In this paper, we propose an approach that overcomes this limitation by taking advantage of the fact that, in practice, some prior on the camera pose is often available. We model it as a Gaussian Mixture Model that we progressively refine by hypothesizing new correspondences. This rapidly reduces the number of potential matches for each 3D point a...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Francesc Moreno-Noguer, Vincent Lepetit, Pascal Fua
• Keywords: Camera Pose, Computer Vision, ECCV 2008, Gaussian Mixture Model, Most Current Approaches, Pose Space, Suitable Objective Function