In the literature, points and conics have been major features for camera
geometric calibration. Although conics are more informative features than
points, the loss of the conic property under distortion has critically limited
the utility of conic features in camera calibration. Many existing approaches
addressed conic-based calibration by ignoring distortion or introducing 3D
spherical targets to circumvent this limitation. In this paper, we present a
novel formulation for conic-based calibration using moments. Our derivation is
based on the mathematical finding that the first moment can be estimated
without bias even under distortion. This allows us to track moment changes
during projection and distortion, ensuring the preservation of the first moment
of the distorted conic. With an unbiased estimator, the circular patterns can
be accurately detected at the sub-pixel level and can now be fully exploited
for an entire calibration pipeline, resulting in significantly improved
calibration. The entire code is readily available from
github.com/ChaehyeonSong/discocal.

通过使用矩进行基于圆锥的相机校准，我们能够在失真下保持扭曲圆锥的第一矩，并利用此方法进行更准确的相机校准。

相机标定中畸变圆锥的无偏估计器

Unbiased Estimator for Distorted Conics in Camera Calibration

Surround-view system (SVS) is widely used in the Advanced Driver Assistance
System (ADAS). SVS uses four fisheye lenses to monitor real-time scenes around
the vehicle. However, accurate intrinsic and extrinsic parameter estimation is
required for the proper functioning of the system. At present, the intrinsic
calibration can be pipeline by utilizing checkerboard algorithm, while
extrinsic calibration is still immature. Therefore, we proposed a specific
calibration pipeline to estimate extrinsic parameters robustly. This scheme
takes a driving sequence of four cameras as input. It firstly utilizes lane
line to roughly estimate each camera pose. Considering the environmental
condition differences in each camera, we separately select strategies from two
methods to accurately estimate the extrinsic parameters. To achieve accurate
estimates for both front and rear camera, we proposed a method that mutually
iterating line detection and pose estimation. As for bilateral camera, we
iteratively adjust the camera pose and position by minimizing texture and edge
error between ground projections of adjacent cameras. After estimating the
extrinsic parameters, the surround-view image can be synthesized by
homography-based transformation. The proposed pipeline can robustly estimate
the four SVS camera extrinsic parameters in real driving environments. In
addition, to evaluate the proposed scheme, we build a surround-view fisheye
dataset, which contains 40 videos with 32,000 frames, acquired from different
real traffic scenarios. All the frames in each video are manually labeled with
lane annotation, with its GT extrinsic parameters. Moreover, this surround-view
dataset could be used by other researchers to evaluate their performance. The
dataset will be available soon.

提出了一种针对车载环视系统（SVS）的校准流程，通过使用巡线检测和姿态估计相互迭代的方法，能够在真实行驶环境中稳健地估计出四个 SVS 相机的外部参数，并构建了包含 40 个视频的车载环视鱼眼数据集，可供其他研究人员用于评估性能。

全景视图系统摄像机校准：基准和数据集

Camera calibration for the surround-view system: a benchmark and dataset

Camera calibration is an essential first step in setting up 3D Computer
Vision systems. Commonly used parametric camera models are limited to a few
degrees of freedom and thus often do not optimally fit to complex real lens
distortion. In contrast, generic camera models allow for very accurate
calibration due to their flexibility. Despite this, they have seen little use
in practice. In this paper, we argue that this should change. We propose a
calibration pipeline for generic models that is fully automated, easy to use,
and can act as a drop-in replacement for parametric calibration, with a focus
on accuracy. We compare our results to parametric calibrations. Considering
stereo depth estimation and camera pose estimation as examples, we show that
the calibration error acts as a bias on the results. We thus argue that in
contrast to current common practice, generic models should be preferred over
parametric ones whenever possible. To facilitate this, we released our
calibration pipeline at this https URL,
making both easy-to-use and accurate camera calibration available to everyone.

提出了一种用于通用相机模型的标定流程，该流程完全自动化，易于使用，可作为参数标定的替代方案，并且可以提高标定精度。该模型应在可能的情况下更优先于参数模型，并且该标定流程已在链接中公开发表。