The precise estimation of camera poses within large camera networks is a
foundational problem in computer vision and robotics, with broad applications
spanning autonomous navigation, surveillance, and augmented reality. In this
paper, we introduce a novel methodology that extends state-of-the-art Pose
Graph Optimization (PGO) techniques. Departing from the conventional PGO
paradigm, which primarily relies on camera-camera edges, our approach centers
on the introduction of a dynamic element - any rigid object free to move in the
scene - whose pose can be reliably inferred from a single image. Specifically,
we consider the bipartite graph encompassing cameras, object poses evolving
dynamically, and camera-object relative transformations at each time step. This
shift not only offers a solution to the challenges encountered in directly
estimating relative poses between cameras, particularly in adverse
environments, but also leverages the inclusion of numerous object poses to
ameliorate and integrate errors, resulting in accurate camera pose estimates.
Though our framework retains compatibility with traditional PGO solvers, its
efficacy benefits from a custom-tailored optimization scheme. To this end, we
introduce an iterative primal-dual algorithm, capable of handling large graphs.
Empirical benchmarks, conducted on a new dataset of simulated indoor
environments, substantiate the efficacy and efficiency of our approach.

通过引入一个动态元素 - 任意场景中可以自由移动的刚性物体，我们提出了一种新颖的方法来精确估计大型摄像头网络中的摄像头姿势，该方法可以可靠地从单张图像中推测出该物体的姿势，并解决了在直接估计摄像头之间相对姿势时遇到的挑战，从而实现了准确的摄像头姿势估计。

VICAN：大规模相机网络的高效标定算法

VICAN: Very Efficient Calibration Algorithm for Large Camera Networks

We propose a method for estimating the 6DoF pose of a rigid object with an
available 3D model from a single RGB image. Unlike classical
correspondence-based methods which predict 3D object coordinates at pixels of
the input image, the proposed method predicts 3D object coordinates at 3D query
points sampled in the camera frustum. The move from pixels to 3D points, which
is inspired by recent PIFu-style methods for 3D reconstruction, enables
reasoning about the whole object, including its (self-)occluded parts. For a 3D
query point associated with a pixel-aligned image feature, we train a
fully-connected neural network to predict: (i) the corresponding 3D object
coordinates, and (ii) the signed distance to the object surface, with the first
defined only for query points in the surface vicinity. We call the mapping
realized by this network as Neural Correspondence Field. The object pose is
then robustly estimated from the predicted 3D-3D correspondences by the
Kabsch-RANSAC algorithm. The proposed method achieves state-of-the-art results
on three BOP datasets and is shown superior especially in challenging cases
with occlusion. The project website is at: this http URL.

本文提出一种方法，通过单个 RGB 图像估计刚性物体的六自由度姿态，其中通过 3D 模型在摄像机视锥中采样三维查询点预测三维物体坐标，并借助全连接神经网络来实现神经对应场，最终使用 Kabsch-RANSAC 算法实现姿态估计，该方法在三个 BOP 数据集中都取得了最优结果。

神经对应场用于物体姿态估计

Neural Correspondence Field for Object Pose Estimation

The pose of a rigid object is usually regarded as a rigid transformation,
described by a translation and a rotation. However, equating the pose space
with the space of rigid transformations is in general abusive, as it does not
account for objects with proper symmetries -- which are common among man-made
objects.In this article, we define pose as a distinguishable static state of an
object, and equate a pose with a set of rigid transformations. Based solely on
geometric considerations, we propose a frame-invariant metric on the space of
possible poses, valid for any physical rigid object, and requiring no arbitrary
tuning. This distance can be evaluated efficiently using a representation of
poses within an Euclidean space of at most 12 dimensions depending on the
object's symmetries. This makes it possible to efficiently perform neighborhood
queries such as radius searches or k-nearest neighbor searches within a large
set of poses using off-the-shelf methods. Pose averaging considering this
metric can similarly be performed easily, using a projection function from the
Euclidean space onto the pose space. The practical value of those theoretical
developments is illustrated with an application of pose estimation of instances
of a 3D rigid object given an input depth map, via a Mean Shift procedure.

本文提出了一种基于几何考虑的、用于描述刚体物体位姿的不依赖于任何人为调整的、基于刚体对称性的不变度量，可以有效地执行大量姿态的查询，如半径搜索或 k 最近邻搜索，并且可以用于简单的姿态平均，文中还提到了一种基于该度量方法的 3D 刚体姿态估计技术。