用激光雷达约束3D高斯点云几何，提升相机-激光雷达标定精度
Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration
- 融合多视角激光雷达数据提供稠密深度监督
- 阻断光度梯度对高斯空间参数的更新，防止几何失真
- 在公开驾驶数据集上显著优于现有无靶标标定方法
精确的激光雷达-相机标定对多模态感知至关重要。无靶标方法避免了人工设置，但仍受限于跨模态判别特征稀缺。近期方法通过在可微分模型中重建场景，利用密集光度监督实现外参优化。其中，3D高斯点阵（3DGS）被广泛用作几何代理，在单一可微框架中桥接激光雷达与相机。然而，由于3DGS最初设计用于新视角合成，现有方法更关注渲染质量，导致代理几何偏离真实激光雷达结构。本文提出一种框架，通过聚合多视角激光雷达观测提供稠密深度监督，并阻断光度梯度对高斯空间参数的更新，以保持代理的度量几何。我们在公开驾驶数据集上验证了该方法，结果表明其在标定精度上持续优于现有无靶标方法。
原文摘要 · Abstract (English)
Accurate LiDAR-camera calibration is essential for robust multi-modal perception. Targetless approaches avoid manual setup but remain limited by the scarcity of discriminative cross-modal features. Recent methods address this by reconstructing the scene within a differentiable model, enabling extrinsic optimization through dense photometric supervision. Among these, 3D Gaussian Splatting (3DGS) has been widely adopted as a geometric proxy that bridges LiDAR and camera within a single differentiable framework. However, since 3DGS was originally designed for novel view synthesis, existing methods tend to prioritize rendering quality, causing the proxy geometry to drift from the true LiDAR structure. We propose a framework that preserves the metric geometry of the Gaussian proxy by aggregating multi-view LiDAR observations for dense depth supervision and blocking photometric gradients from updating the Gaussian spatial parameters. We validate our method on public driving datasets, where it consistently outperforms existing targetless methods in calibration accuracy.
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