无需标定物和人工干预，实时自动完成激光雷达与摄像头的高精度标定。
CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement
- 基于特征匹配与空间位置信息，自动建立点云与图像间的对应关系。
- 通过迭代优化和注意力机制，显著提升复杂场景下的标定精度。
- 适合自动驾驶等需要快速部署与自适应校准的真实环境应用。
精确的多传感器标定对自动驾驶和智能交通等应用中的鲁棒感知系统至关重要。现有激光雷达-相机标定方法常依赖人工放置标定物、预估参数或复杂的数据预处理，限制了其在真实场景中的可扩展性和适应性。本文提出一种完全自动、无标定物且支持在线标定的框架CalibRefine，直接处理原始激光雷达点云与相机图像。该方法分为四个阶段：(1) 共同特征判别器利用相对空间位置、视觉外观嵌入和语义类别线索，识别并生成可靠的点云-图像对应关系；(2) 基于粗略单应性估计初始变换矩阵，为后续优化提供基础；(3) 迭代式精修随着新帧输入逐步提升对齐精度；(4) 基于视觉变压器与交叉注意力机制的注意力精修，有效处理非平面畸变。在两个城市交通数据集上的大量实验表明，CalibRefine仅需极少人工干预即可实现高精度标定，性能优于现有无标定物方法，并达到或超过人工调参基准。结果证明，在无需真值矩阵或繁琐预处理的前提下，结合对象级特征匹配、迭代优化与自监督注意力精修，可在复杂真实环境中实现可靠传感器对齐。代码已开源：https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration
原文摘要 · Abstract (English)
Accurate multi-sensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing LiDAR-camera calibration methods often rely on manually placed targets, preliminary parameter estimates, or intensive data preprocessing, limiting their scalability and adaptability in real-world settings. In this work, we propose a fully automatic, targetless, and online calibration framework, CalibRefine, which directly processes raw LiDAR point clouds and camera images. Our approach is divided into four stages: (1) a Common Feature Discriminator that leverages relative spatial positions, visual appearance embeddings, and semantic class cues to identify and generate reliable LiDAR-camera correspondences, (2) a coarse homography-based calibration that uses the matched feature correspondences to estimate an initial transformation between the LiDAR and camera frames, serving as the foundation for further refinement, (3) an iterative refinement to incrementally improve alignment as additional data frames become available, and (4) an attention-based refinement that addresses non-planar distortions by leveraging a Vision Transformer and cross-attention mechanisms. Extensive experiments on two urban traffic datasets demonstrate that CalibRefine achieves high-precision calibration with minimal human input, outperforming state-of-the-art targetless methods and matching or surpassing manually tuned baselines. Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment in complex real-world conditions without ground-truth matrices or elaborate preprocessing. Code is available at https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration
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