The research on extrinsic calibration between Light Detection and Ranging(LiDAR) and camera are being promoted to a more accurate, automatic and generic manner. Since deep learning has been employed in calibration, the restrictions on the scene are greatly reduced. However, data driven method has the drawback of low transfer-ability. It cannot adapt to dataset variations unless additional training is taken. With the advent of foundation model, this problem can be significantly mitigated. By using the Segment Anything Model(SAM), we propose a novel LiDAR-camera calibration method, which requires zero extra training and adapts to common scenes. With an initial guess, we opimize the extrinsic parameter by maximizing the consistency of points that are projected inside each image mask. The consistency includes three properties of the point cloud: the intensity, normal vector and categories derived from some segmentation methods. The experiments on different dataset have demonstrated the generality and comparable accuracy of our method. The code is available at https://github.com/OpenCalib/CalibAnything.

翻译：激光雷达与相机之间的外参标定研究正朝着更精确、更自动化和更通用的方向发展。随着深度学习在标定中的应用，对场景的限制已大幅减少。然而，数据驱动方法存在迁移能力低的缺点，除非进行额外训练，否则无法适应数据集的差异。基础模型的出现显著缓解了这一问题。通过使用Segment Anything模型（SAM），我们提出了一种新颖的激光雷达-相机标定方法，该方法无需任何额外训练，并能适应常见场景。在给定初始猜测值的情况下，我们通过最大化投影到每个图像掩膜内部的点云一致性来优化外参参数。该一致性包括点云的三类属性：强度、法向量以及通过若干分割方法导出的类别。在不同数据集上的实验证明了我们方法的通用性和相当的精度。代码已开源在https://github.com/OpenCalib/CalibAnything。