Camera localization methods based on retrieval, local feature matching, and
3D structure-based pose estimation are accurate but require high storage, are
slow, and are not privacy-preserving. A method based on scene landmark
detection (SLD) was recently proposed to address these limitations. It involves
training a convolutional neural network (CNN) to detect a few predetermined,
salient, scene-specific 3D points or landmarks and computing camera pose from
the associated 2D-3D correspondences. Although SLD outperformed existing
learning-based approaches, it was notably less accurate than 3D structure-based
methods. In this paper, we show that the accuracy gap was due to insufficient
model capacity and noisy labels during training. To mitigate the capacity
issue, we propose to split the landmarks into subgroups and train a separate
network for each subgroup. To generate better training labels, we propose using
dense reconstructions to estimate visibility of scene landmarks. Finally, we
present a compact architecture to improve memory efficiency. Accuracy wise, our
approach is on par with state of the art structure based methods on the
INDOOR-6 dataset but runs significantly faster and uses less storage. Code and
models can be found at this https URL

基于场景地标检测的摄像头定位方法，采用卷积神经网络（CNN）检测少量特定的场景 3D 点或地标，并从相关的 2D-3D 对应中计算摄像头姿态，具有与基于 3D 结构的方法相当的准确性，但速度更快且使用存储空间更少。

相机定位的场景标记检测改进

Improved Scene Landmark Detection for Camera Localization

Visual localization is the task of accurate camera pose estimation in a known
scene. It is a key problem in computer vision and robotics, with applications
including self-driving cars, Structure-from-Motion, SLAM, and Mixed Reality.
Traditionally, the localization problem has been tackled using 3D geometry.
Recently, end-to-end approaches based on convolutional neural networks have
become popular. These methods learn to directly regress the camera pose from an
input image. However, they do not achieve the same level of pose accuracy as 3D
structure-based methods. To understand this behavior, we develop a theoretical
model for camera pose regression. We use our model to predict failure cases for
pose regression techniques and verify our predictions through experiments. We
furthermore use our model to show that pose regression is more closely related
to pose approximation via image retrieval than to accurate pose estimation via
3D structure. A key result is that current approaches do not consistently
outperform a handcrafted image retrieval baseline. This clearly shows that
additional research is needed before pose regression algorithms are ready to
compete with structure-based methods.

本研究旨在解决场景中精确的相机姿态估计的视觉定位问题，传统方法使用 3D 几何学，而新近出现的卷积神经网络则直接从输入图像中学习相机姿态，但是并未达到 3D 方法的精度。本论文提出了一种理论模型来预测 pose regression 技术的失败案例，并证实了我们的预测。作者进一步表明，pose regression 与图像检索非常相似，而不是与 3D 结构的准确姿态估计。最重要的是，当前方法并不能持续超越手工图像检索基线，因此需要进一步的研究。