The process of camera calibration involves estimating the intrinsic and
extrinsic parameters, which are essential for accurately performing tasks such
as 3D reconstruction, object tracking and augmented reality. In this work, we
propose a novel constraints-based loss for measuring the intrinsic (focal
length: $(f_x, f_y)$ and principal point: $(p_x, p_y)$) and extrinsic
(baseline: ($b$), disparity: ($d$), translation: $(t_x, t_y, t_z)$, and
rotation specifically pitch: $(\theta_p)$) camera parameters. Our novel
constraints are based on geometric properties inherent in the camera model,
including the anatomy of the projection matrix (vanishing points, image of
world origin, axis planes) and the orthonormality of the rotation matrix. Thus
we proposed a novel Unsupervised Geometric Constraint Loss (UGCL) via a
multitask learning framework. Our methodology is a hybrid approach that employs
the learning power of a neural network to estimate the desired parameters along
with the underlying mathematical properties inherent in the camera projection
matrix. This distinctive approach not only enhances the interpretability of the
model but also facilitates a more informed learning process. Additionally, we
introduce a new CVGL Camera Calibration dataset, featuring over 900
configurations of camera parameters, incorporating 63,600 image pairs that
closely mirror real-world conditions. By training and testing on both synthetic
and real-world datasets, our proposed approach demonstrates improvements across
all parameters when compared to the state-of-the-art (SOTA) benchmarks. The
code and the updated dataset can be found here:
this https URL

相机标定是估计内参和外参参数的过程，本文提出了一种基于几何约束的损失函数来测量相机参数，并通过神经网络学习框架进行优化，实验结果表明，在合成和实际数据集上，相比最先进的基准模型，我们的方法在所有参数上都有所改进。

基于旋转和投影矩阵的几何约束相机标定

Camera Calibration through Geometric Constraints from Rotation and  Projection Matrices

Neural Radiance Fields (NeRF) employ multi-view images for 3D scene
representation and have shown remarkable performance. As one of the primary
sources of multi-view images, multi-camera systems encounter challenges such as
varying intrinsic parameters and frequent pose changes. Most previous
NeRF-based methods often assume a global unique camera and seldom consider
scenarios with multiple cameras. Besides, some pose-robust methods still remain
susceptible to suboptimal solutions when poses are poor initialized. In this
paper, we propose MC-NeRF, a method can jointly optimize both intrinsic and
extrinsic parameters for bundle-adjusting Neural Radiance Fields. Firstly, we
conduct a theoretical analysis to tackle the degenerate case and coupling issue
that arise from the joint optimization between intrinsic and extrinsic
parameters. Secondly, based on the proposed solutions, we introduce an
efficient calibration image acquisition scheme for multi-camera systems,
including the design of calibration object. Lastly, we present a global
end-to-end network with training sequence that enables the regression of
intrinsic and extrinsic parameters, along with the rendering network. Moreover,
most existing datasets are designed for unique camera, we create a new dataset
that includes four different styles of multi-camera acquisition systems,
allowing readers to generate custom datasets. Experiments confirm the
effectiveness of our method when each image corresponds to different camera
parameters. Specifically, we adopt up to 110 images with 110 different
intrinsic and extrinsic parameters, to achieve 3D scene representation without
providing initial poses. The Code and supplementary materials are available at
this https URL

该研究论文提出了一种称为 MC-NeRF 的方法，能够联合优化束调整神经辐射场的内参和外参参数，解决了多相机系统的挑战，并提供了一个有效的校准图像采集方案，实现了不提供初始姿势的三维场景表示。

MC-NeRF：用于多摄像头图像采集系统的多摄像头神经辐射场

MC-NeRF: Muti-Camera Neural Radiance Fields for Muti-Camera Image  Acquisition Systems

Color-depth cameras (RGB-D cameras) have become the primary sensors in most
robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and
extrinsic calibration that generally does not meet the accuracy requirements
needed by many robotics applications (e.g., highly accurate 3D environment
reconstruction and mapping, high precision object recognition and localization,
...). In this paper, we propose a human-friendly, reliable and accurate
calibration framework that enables to easily estimate both the intrinsic and
extrinsic parameters of a general color-depth sensor couple. Our approach is
based on a novel two components error model. This model unifies the error
sources of RGB-D pairs based on different technologies, such as
structured-light 3D cameras and time-of-flight cameras. Our method provides
some important advantages compared to other state-of-the-art systems: it is
general (i.e., well suited for different types of sensors), based on an easy
and stable calibration protocol, provides a greater calibration accuracy, and
has been implemented within the ROS robotics framework. We report detailed
experimental validations and performance comparisons to support our statements.

本论文提出了一种基于新型两个组件误差模型的，人性化，可靠且准确的校准框架，能够很容易地估计一般颜色深度传感器的内部和外部参数，该方法相对于其他现有系统具有更高的校准精度，已经在 ROS 机器人框架中实现。