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Camera relocalisation is an important problem in computer vision, withapplications in simultaneous localisation and mapping, virtual/augmentedreality and navigation. Common techniques either match the current imageagainst keyframes with known poses coming from a tracker, or establish 2D-to-3Dcorrespondences between keypoints in the current image and points in the scenein order to estimate the camera pose. Recently, regression forests have becomea popular alternative to establish such correspondences. They achieve accurateresults, but must be trained offline on the target scene, preventingrelocalisation in new environments. In this paper, we show how to circumventthis limitation by adapting a pre-trained forest to a new scene on the fly. Ouradapted forests achieve relocalisation performance that is on par with that ofoffline forests, and our approach runs in under 150ms, making it desirable forreal-time systems that require online relocalisation.

On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation