Reliable multimodal sensor fusion algorithms re- quire accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have proven to provide precise and robust results. Nevertheless, such methods are inherently slow to train given the high compu- tational overhead caused by the large number of sampled points required for volume rendering. With the recent introduction of 3D Gaussian Splatting as a faster alternative to implicit representation methods, we propose to leverage this new ren- dering approach to achieve faster multi-sensor calibration. We introduce 3DGS-Calib, a new calibration method that relies on the speed and rendering accuracy of 3D Gaussian Splatting to achieve multimodal spatiotemporal calibration that is accurate, robust, and with a substantial speed-up compared to methods relying on implicit neural representations. We demonstrate the superiority of our proposal with experimental results on sequences from KITTI-360, a widely used driving dataset.

翻译：可靠的多模态传感器融合算法需要精确的时空标定。近年来，基于隐式神经表示的无目标标定技术已被证明能够提供精确且鲁棒的结果。然而，此类方法因体积渲染所需的大量采样点导致高昂的计算开销，本质上训练速度缓慢。随着3D高斯泼溅作为隐式表示方法更快速的替代方案被引入，我们提出利用这种新型渲染方法实现更快的多传感器标定。我们提出3DGS-Calib——一种依赖3D高斯泼溅的速度与渲染精度实现多模态时空标定的新方法，该方法不仅精确鲁棒，而且相较依赖隐式神经表示的方法实现了显著加速。通过在广泛使用的驾驶数据集KITTI-360上的序列实验，我们证明了所提方法的优越性。