用于在线相机重新定位的回归森林的动态适应
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
摘要：
摄像机重定位是计算机视觉中的一个重要问题，在同时定位和地图绘制、虚拟/增强现实和导航中具有应用。常见的技术要么将当前图像与来自跟踪器的具有已知姿态的关键帧进行匹配，要么在当前图像中的关键点与场景中的点之间建立2D到3D对应关系，以便估计相机姿态。最近，回归森林已成为建立这种对应关系的流行替代方案。它们可以获得准确的结果，但必须在目标场景中进行离线训练，防止在新环境中重新定位。在本文中，我们展示了如何通过使预先训练的森林适应新场景来规避这一限制。我们的适应森林实现了与离线森林相当的重新定位性能，并且我们的方法在150 ms以下运行，这对于需要在线重新定位的实时系统来说是理想的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
