Fast 2D-to-3D matching with camera pose voting for 3D object identification

摘要

In this paper, we propose a fast non-iterative camera pose voting method for 3D object identification. The proposed method improves the accuracy and speed upon the conventional local feature based 2D-to-3D matching between a 2D image and a 3D model reconstructed by the structure-from-motion (SfM) pipeline. Instead of performing iterative RANSAC based method for geometric verification, the proposed method computes a hypothesis of camera pose from each feature correspondence between the query image and the 3D model. The camera pose is computed using scale, orientation and coordinate of the local features, calibrated by the camera matrix of the database image used to construct the 3D model. Then the most likely hypothesis is found by carrying out a two-stage clustering on the estimated camera poses in the parameter space. Experiment results on the 3D machine datasets show that our method improves the identification accuracy from 82.8% to 84.9% when FPR is 1%, compared with conventional RANSAC based method. In addition, the processing speed for the geometric verification is improved up to 25 times compared to the conventional method.