Exploiting random rgb and sparse features for camera pose estimation

Abstract

We address the problem of estimating camera pose relative to a known scene, given a single RGB image. We extend recent advances in scene coordinate regression forests for camera relocalization in RGB-D images to use RGB features, enabling camera relocalization from a single RGB image. Furthermore, we integrate random RGB features and sparse feature matching in an efficient and accurate way, broadening the method for fast sports camera calibration in highly dynamic scenes. We evaluate our method on both static, small scale and dynamic, large scale datasets with challenging camera poses. The proposed method is compared with several strong baselines. Experiment results demonstrate the efficacy of our approach, showing superior or on-par performance with the state of the art.