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

We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM.

本文提出了一种新的深度卷积网络 LO-Net，其可以进行实时激光雷达里程计估计，并通过端到端的方式进行训练。LO-Net 具有一种新的掩码加权的几何约束损失函数，可以有效地学习 LO 估计的特征表达，并可以隐式地利用数据中的时序依赖和动态性。我们还设计了一个扫描到地图的模块，利用 LO-Net 中所学习的几何和语义信息，以提高估计精度。基准数据集上的实验表明，LO-Net 胜过现有的基于学习的方法，并具有与 LOAM 的现有状态 - of-the-art 几何方法相似的准确性。