On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
This enables real-time camera relocalisation for applications like SLAM and AR, though it is incremental as it builds on existing regression forest methods.
The paper tackles the problem of camera relocalisation in new environments by adapting pre-trained regression forests on the fly, achieving performance comparable to offline forests with a runtime under 150ms.
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation. Common techniques either match the current image against keyframes with known poses coming from a tracker, or establish 2D-to-3D correspondences between keypoints in the current image and points in the scene in order to estimate the camera pose. Recently, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but must be trained offline on the target scene, preventing relocalisation in new environments. In this paper, we show how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. Our adapted forests achieve relocalisation performance that is on par with that of offline forests, and our approach runs in under 150ms, making it desirable for real-time systems that require online relocalisation.