Pose regression networks predict the camera pose of a query image relative to
a known environment. Within this family of methods, absolute pose regression
(APR) has recently shown promising accuracy in the range of a few centimeters
in position error. APR networks encode the scene geometry implicitly in their
weights. To achieve high accuracy, they require vast amounts of training data
that, realistically, can only be created using novel view synthesis in a
days-long process. This process has to be repeated for each new scene again and
again. We present a new approach to pose regression, map-relative pose
regression (marepo), that satisfies the data hunger of the pose regression
network in a scene-agnostic fashion. We condition the pose regressor on a
scene-specific map representation such that its pose predictions are relative
to the scene map. This allows us to train the pose regressor across hundreds of
scenes to learn the generic relation between a scene-specific map
representation and the camera pose. Our map-relative pose regressor can be
applied to new map representations immediately or after mere minutes of
fine-tuning for the highest accuracy. Our approach outperforms previous pose
regression methods by far on two public datasets, indoor and outdoor. Code is
available: this https URL

相对于已知环境，姿态回归网络通过预测查询图像的相机姿态。我们提出了一种新的姿态回归方法，地图相对姿态回归（marepo），通过使用场景特定的地图表示，将姿态回归器的预测相对于场景地图。我们的方法在室内和室外两个公共数据集上远远优于之前的姿态回归方法。

基于地图的姿态回归视觉重定位

Map-Relative Pose Regression for Visual Re-Localization

We introduce a novel neural volumetric pose feature, termed PoseMap, designed
to enhance camera localization by encapsulating the information between images
and the associated camera poses. Our framework leverages an Absolute Pose
Regression (APR) architecture, together with an augmented NeRF module. This
integration not only facilitates the generation of novel views to enrich the
training dataset but also enables the learning of effective pose features.
Additionally, we extend our architecture for self-supervised online alignment,
allowing our method to be used and fine-tuned for unlabelled images within a
unified framework. Experiments demonstrate that our method achieves 14.28% and
20.51% performance gain on average in indoor and outdoor benchmark scenes,
outperforming existing APR methods with state-of-the-art accuracy.

引入了一种新颖的神经体姿态特征 ——PoseMap，旨在通过囊括图像之间和相关摄像机姿态之间的信息增强摄像机定位能力。该框架利用了绝对姿态回归（APR）架构和增强的 NeRF 模块，不仅有助于生成丰富的训练数据集来丰富训练，在线自监督对齐的扩展还能允许该方法在无标签图像上使用和微调。实验证明，我们的方法在室内和室外基准场景上平均性能分别提高了 14.28% 和 20.51%，超过了现有的具有最先进精度的 APR 方法。

学习神经体积位姿特征用于相机定位

Learning Neural Volumetric Pose Features for Camera Localization

The localization of objects is a crucial task in various applications such as
robotics, virtual and augmented reality, and the transportation of goods in
warehouses. Recent advances in deep learning have enabled the localization
using monocular visual cameras. While structure from motion (SfM) predicts the
absolute pose from a point cloud, absolute pose regression (APR) methods learn
a semantic understanding of the environment through neural networks. However,
both fields face challenges caused by the environment such as motion blur,
lighting changes, repetitive patterns, and feature-less structures. This study
aims to address these challenges by incorporating additional information and
regularizing the absolute pose using relative pose regression (RPR) methods.
The optical flow between consecutive images is computed using the Lucas-Kanade
algorithm, and the relative pose is predicted using an auxiliary small
recurrent convolutional network. The fusion of absolute and relative poses is a
complex task due to the mismatch between the global and local coordinate
systems. State-of-the-art methods fusing absolute and relative poses use pose
graph optimization (PGO) to regularize the absolute pose predictions using
relative poses. In this work, we propose recurrent fusion networks to optimally
align absolute and relative pose predictions to improve the absolute pose
prediction. We evaluate eight different recurrent units and construct a
simulation environment to pre-train the APR and RPR networks for better
generalized training. Additionally, we record a large database of different
scenarios in a challenging large-scale indoor environment that mimics a
warehouse with transportation robots. We conduct hyperparameter searches and
experiments to show the effectiveness of our recurrent fusion method compared
to PGO.

本文提出了一种使用相对位移回归来规范绝对位姿估计的新方法，采用递归融合神经网络来更好地整合绝对和相对估计姿态，实验结果表明这种方法比使用姿态图优化的方法更为有效。