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

We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and
accurate joint map of dense 3D models for foreground objects, and sparse
landmark points to represent the background. DSP-SLAM takes as input the 3D
point cloud reconstructed by a feature-based SLAM system and equips it with the
ability to enhance its sparse map with dense reconstructions of detected
objects. Objects are detected via semantic instance segmentation, and their
shape and pose is estimated using category-specific deep shape embeddings as
priors, via a novel second order optimization. Our object-aware bundle
adjustment builds a pose-graph to jointly optimize camera poses, object
locations and feature points. DSP-SLAM can operate at 10 frames per second on 3
different input modalities: monocular, stereo, or stereo+LiDAR. We demonstrate
DSP-SLAM operating at almost frame rate on monocular-RGB sequences from the
Friburg and Redwood-OS datasets, and on stereo+LiDAR sequences on the KITTI
odometry dataset showing that it achieves high-quality full object
reconstructions, even from partial observations, while maintaining a consistent
global map. Our evaluation shows improvements in object pose and shape
reconstruction with respect to recent deep prior-based reconstruction methods
and reductions in camera tracking drift on the KITTI dataset.

DSP-SLAM 是一种面向对象的 SLAM 系统，通过语义实例分割检测物体并使用特定于类别的深度形状嵌入来估计其姿态和形状，从而实现丰富和准确的 3D 物体建图，能够在单目、双目或双目 + LiDAR 输入模式下以接近帧率的速度处理图像序列，并在 KITTI 轨迹数据集上显示其在姿态和形状重构方面的显著改善。