Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method
  Using Segment Anything
Zhaotong Luo Guohang Yan Yikang Li
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.