This work introduces a novel end-to-end approach for estimating extrinsic
parameters of cameras in multi-camera setups on real-life sports fields. We
identify the source of significant calibration errors in multi-camera
environments and address the limitations of existing calibration methods,
particularly the disparity between theoretical models and actual sports field
characteristics. We propose the Evolutionary Stitched Camera calibration (ESC)
algorithm to bridge this gap. It consists of image segmentation followed by
evolutionary optimization of a novel loss function, providing a unified and
accurate multi-camera calibration solution with high visual fidelity. The
outcome allows the creation of virtual stitched views from multiple video
sources, being as important for practical applications as numerical accuracy.
We demonstrate the superior performance of our approach compared to
state-of-the-art methods across diverse real-life football fields with varying
physical characteristics.

该研究提出了一种新颖的端到端方法，用于估计多摄像机设置中实际运动场上的摄像机外参参数。通过识别多摄像机环境中校准误差的来源并解决现有校准方法的局限性，尤其是理论模型与实际运动场特点之间的差异，我们提出了演化缝合摄像机校准（ESC）算法。该算法包括图像分割和演化优化新型损失函数，为多摄像机校准提供了统一准确的解决方案，并具有高视觉保真度。这一成果能够从多个视频源创建虚拟拼接视图，对于实际应用和数值准确性同等重要。我们在具有不同物理特征的各种真实足球场地上展示了我们方法相对于最先进方法的卓越性能。

野外环境下的进化缝合相机标定

ESC: Evolutionary Stitched Camera Calibration in the Wild

Multi-camera setups find widespread use across various applications, such as
autonomous driving, as they greatly expand sensing capabilities. Despite the
fast development of Neural radiance field (NeRF) techniques and their wide
applications in both indoor and outdoor scenes, applying NeRF to multi-camera
systems remains very challenging. This is primarily due to the inherent
under-calibration issues in multi-camera setup, including inconsistent imaging
effects stemming from separately calibrated image signal processing units in
diverse cameras, and system errors arising from mechanical vibrations during
driving that affect relative camera poses. In this paper, we present UC-NeRF, a
novel method tailored for novel view synthesis in under-calibrated multi-view
camera systems. Firstly, we propose a layer-based color correction to rectify
the color inconsistency in different image regions. Second, we propose virtual
warping to generate more viewpoint-diverse but color-consistent virtual views
for color correction and 3D recovery. Finally, a spatiotemporally constrained
pose refinement is designed for more robust and accurate pose calibration in
multi-camera systems. Our method not only achieves state-of-the-art performance
of novel view synthesis in multi-camera setups, but also effectively
facilitates depth estimation in large-scale outdoor scenes with the synthesized
novel views.

UC-NeRF 是一种专门针对欠校准多视角摄像系统的新视角合成方法，通过图层级颜色校正、虚拟畸变、时空约束的位姿细化等技术实现了在多摄像头环境下的优秀新视角合成和大规模室外场景的深度估计。

UC-NeRF：用于自动驾驶中未校准多视角相机的神经辐射场

UC-NeRF: Neural Radiance Field for Under-Calibrated multi-view cameras  in autonomous driving

Neural networks build the foundation of several intelligent systems, which,
however, are known to be easily fooled by adversarial examples. Recent advances
made these attacks possible even in air-gapped scenarios, where the autonomous
system observes its surroundings by, e.g., a camera. We extend these ideas in
our research and evaluate the robustness of multi-camera setups against such
physical adversarial examples. This scenario becomes ever more important with
the rise in popularity of autonomous vehicles, which fuse the information of
several cameras for their driving decision. While we find that multi-camera
setups provide some robustness towards past attack methods, we see that this
advantage reduces when optimizing on multiple perspectives at once. We propose
a novel attack method that we call Transcender-MC, where we incorporate online
3D renderings and perspective projections in the training process. Moreover, we
motivate that certain data augmentation techniques can facilitate the
generation of successful adversarial examples even further. Transcender-MC is
11% more effective in successfully attacking multi-camera setups than
state-of-the-art methods. Our findings offer valuable insights regarding the
resilience of object detection in a setup with multiple cameras and motivate
the need of developing adequate defense mechanisms against them.

我们扩展了现有的研究，在多摄像头设置中评估了多摄像头设置相对于物理对抗样本的鲁棒性，并提出了一种名为 Transcender-MC 的新型攻击方法，该方法在训练过程中融入了在线 3D 渲染和透视投影。比起现有方法，这种攻击方法在成功攻击多摄像头设置方面效果提高了 11％。我们的发现为具有多个摄像头的物体检测的弹性提供了宝贵的见解，并促使人们开发相应的防御机制。