无需标定靶的激光雷达-相机联合标定，用神经高斯点云实现精准对齐。
Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting
- 用神经高斯点云构建场景表示，联合优化传感器位姿。
- 在KITTI-360等数据集上优于现有无靶标方法，精度提升显著。
- 适合需长期运行的自动驾驶系统，可自动修复漂移问题。
精确的激光雷达-相机标定对多传感器系统至关重要。然而，传统方法依赖物理标定靶，难以在真实场景中部署。此外，即使初始标定准确，传感器漂移或外部扰动也会导致外参退化，需定期重新标定。为此，我们提出一种无靶标激光雷达-相机标定方法（TLC-Calib），通过神经高斯场景表示联合优化传感器位姿。将可靠的激光雷达点作为锚点高斯以保持全局结构，辅以额外高斯避免初始化噪声下的局部过拟合。全可微分的管道结合光度与几何正则化，在KITTI-360、Waymo和Fast-LIVO2数据集上持续优于现有无靶标方法。同时，生成的新视角图像一致性更高，反映出外参对齐更优。项目主页：https://www.haebeom.com/tlc-calib-site/。
原文摘要 · Abstract (English)
Accurate LiDAR-camera calibration is crucial for multi-sensor systems. However, traditional methods often rely on physical targets, which are impractical for real-world deployment. Moreover, even carefully calibrated extrinsics can degrade over time due to sensor drift or external disturbances, necessitating periodic recalibration. To address these challenges, we present a Targetless LiDAR-Camera Calibration (TLC-Calib) that jointly optimizes sensor poses with a neural Gaussian-based scene representation. Reliable LiDAR points are frozen as anchor Gaussians to preserve global structure, while auxiliary Gaussians prevent local overfitting under noisy initialization. Our fully differentiable pipeline with photometric and geometric regularization achieves robust and generalizable calibration, consistently outperforming existing targetless methods on the KITTI-360, Waymo, and Fast-LIVO2 datasets. In addition, it yields more consistent Novel View Synthesis results, reflecting improved extrinsic alignment. The project page is available at: https://www.haebeom.com/tlc-calib-site/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。