Image based 6-DOF camera pose estimation with weighted RANSAC 3D

In this work an approach for image based 6-DOF pose estimation, with respect to a given 3D point cloud model, is presented. We use 3D annotated training views of the model from which we extract natural 2D features, which can be matched to the query image 2D features. In the next step typically the Perspective-N-Point Problem in combination with the popular RANSAC algorithm on the given 2D-3D point correspondences is used, to estimate the 6-D pose of the camera in respect to the model. We propose a novel extension of the RANSAC algorithm, named w-RANSAC 3D, which uses known 3D information to weight each match individually. The evaluation shows that w-RANSAC 3D leads to a more robust pose estimation while needing significantly less iterations.