Camera pose estimation is an important problem in computer vision. Common
techniques either match the current image against keyframes with known poses,
directly regress the pose, or establish correspondences between keypoints in
the image and points in the scene to estimate the pose. In recent years,
regression forests have become a popular alternative to establish such
correspondences. They achieve accurate results, but have traditionally needed
to be trained offline on the target scene, preventing relocalisation in new
environments. Recently, we showed how to circumvent this limitation by adapting
a pre-trained forest to a new scene on the fly. The adapted forests achieved
relocalisation performance that was on par with that of offline forests, and
our approach was able to estimate the camera pose in close to real time. In
this paper, we present an extension of this work that achieves significantly
better relocalisation performance whilst running fully in real time. To achieve
this, we make several changes to the original approach: (i) instead of
accepting the camera pose hypothesis without question, we make it possible to
score the final few hypotheses using a geometric approach and select the most
promising; (ii) we chain several instantiations of our relocaliser together in
a cascade, allowing us to try faster but less accurate relocalisation first,
only falling back to slower, more accurate relocalisation as necessary; and
(iii) we tune the parameters of our cascade to achieve effective overall
performance. These changes allow us to significantly improve upon the
performance our original state-of-the-art method was able to achieve on the
well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional
contributions, we present a way of visualising the internal behaviour of our
forests and show how to entirely circumvent the need to pre-train a forest on a
generic scene.

本文提出了一种改进的方法来解决摄像机位姿估计问题，利用回归森林对场景中的关键点进行对应，采用几何方法评分并选择最有前途的假设，使用多个快速但不精确的重新定位器级联，调整参数以实现有效的性能，实现了重定位性能的显着提升。

使用重新本地化级联实时估计新场景中的 RGB-D 摄像机姿态

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a  Relocalisation Cascade

Machine learning techniques, namely convolutional neural networks (CNN) and
regression forests, have recently shown great promise in performing 6-DoF
localization of monocular images. However, in most cases image-sequences,
rather only single images, are readily available. To this extent, none of the
proposed learning-based approaches exploit the valuable constraint of temporal
smoothness, often leading to situations where the per-frame error is larger
than the camera motion. In this paper we propose a recurrent model for
performing 6-DoF localization of video-clips. We find that, even by considering
only short sequences (20 frames), the pose estimates are smoothed and the
localization error can be drastically reduced. Finally, we consider means of
obtaining probabilistic pose estimates from our model. We evaluate our method
on openly-available real-world autonomous driving and indoor localization
datasets.

本文介绍了一种使用循环模型进行短视频聚合并实现相机定位的方法。通过考虑短视频的时间平滑性，不仅可以使位姿估计更加平滑，还可以显著降低相机定位误差，并提出了一种从该模型中获得概率位姿估计的方法。我们在实际的自动驾驶和室内定位数据集上进行了评估。

VidLoc：用于 6DoF 视频剪辑重定位的深度时空模型

VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization

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.

本文介绍了一种基于回归树的摄像头位姿跟踪方法，该方法可以快速适应新环境，并达到与离线方法相当的位姿跟踪效果，适用于需要在线位姿跟踪的实时系统。