We propose a generic event camera calibration framework using image
reconstruction. Instead of relying on blinking LED patterns or external
screens, we show that neural-network-based image reconstruction is well suited
for the task of intrinsic and extrinsic calibration of event cameras. The
advantage of our proposed approach is that we can use standard calibration
patterns that do not rely on active illumination. Furthermore, our approach
enables the possibility to perform extrinsic calibration between frame-based
and event-based sensors without additional complexity. Both simulation and
real-world experiments indicate that calibration through image reconstruction
is accurate under common distortion models and a wide variety of distortion
parameters

使用基于神经网络的图像重建，提出通用的事件相机标定框架，实现内在和外在标定，从而可以进行基于时间和基于帧的传感器之间的内外标定，并证明该方法在不同畸变模型和畸变参数下都非常准确。

如何校准事件相机

How to Calibrate Your Event Camera

This paper presents a new deep-learning based method to simultaneously
calibrate the intrinsic parameters of fisheye lens and rectify the distorted
images. Assuming that the distorted lines generated by fisheye projection
should be straight after rectification, we propose a novel deep neural network
to impose explicit geometry constraints onto processes of the fisheye lens
calibration and the distorted image rectification. In addition, considering the
nonlinearity of distortion distribution in fisheye images, the proposed network
fully exploits multi-scale perception to equalize the rectification effects on
the whole image. To train and evaluate the proposed model, we also create a new
largescale dataset labeled with corresponding distortion parameters and
well-annotated distorted lines. Compared with the state-of-the-art methods, our
model achieves the best published rectification quality and the most accurate
estimation of distortion parameters on a large set of synthetic and real
fisheye images.

本文提出了一种基于深度学习的方法来同时校准鱼眼镜头的内部参数和矫正失真的图像，并进一步针对其非线性失真分布和特定几何形状设计了一种多尺度感知的神经网络，通过新的数据集测试发现该方法相比于其他先进算法能在合成和真实数据的鱼眼图像上较好地矫正并准确估计失真参数。

学习校准鱼眼图像的直线以进行校正

Learning to Calibrate Straight Lines for Fisheye Image Rectification

Elastic distortion of fingerprints has a negative effect on the performance
of fingerprint recognition systems. This negative effect brings inconvenience
to users in authentication applications. However, in the negative recognition
scenario where users may intentionally distort their fingerprints, this can be
a serious problem since distortion will prevent recognition system from
identifying malicious users. Current methods aimed at addressing this problem
still have limitations. They are often not accurate because they estimate
distortion parameters based on the ridge frequency map and orientation map of
input samples, which are not reliable due to distortion. Secondly, they are not
efficient and requiring significant computation time to rectify samples. In
this paper, we develop a rectification model based on a Deep Convolutional
Neural Network (DCNN) to accurately estimate distortion parameters from the
input image. Using a comprehensive database of synthetic distorted samples, the
DCNN learns to accurately estimate distortion bases ten times faster than the
dictionary search methods used in the previous approaches. Evaluating the
proposed method on public databases of distorted samples shows that it can
significantly improve the matching performance of distorted samples.

本文提出了一种基于深度卷积神经网络的指纹形变修正模型，可快速准确地估计图像的形变参数，从而显著提高了扭曲样本的匹配性能。