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.

基于 3D 高斯扩散渲染方法，我们提出了一种新的多模态时空校准方法 3DGS-Calib，该方法在精确性、稳健性和速度上都优于依赖于隐式神经表示方法的传统方法。我们通过对 KITTI-360 驾驶数据集的实验结果证明了我们的方法的优越性。

3DGS-Calib：多模态时空校准的 3D 高斯喷涂

3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal  Calibration

Constantly locating moving objects, i.e., geospatial tracking, is essential
for autonomous building infrastructure. Accurate and robust geospatial tracking
often leverages multimodal sensor fusion algorithms, which require large
datasets with time-aligned, synchronized data from various sensor types.
However, such datasets are not readily available. Hence, we propose GDTM, a
nine-hour dataset for multimodal object tracking with distributed multimodal
sensors and reconfigurable sensor node placements. Our dataset enables the
exploration of several research problems, such as optimizing architectures for
processing multimodal data, and investigating models' robustness to adverse
sensing conditions and sensor placement variances. A GitHub repository
containing the code, sample data, and checkpoints of this work is available at
this https URL

提出了使用多模态传感器进行多模态物体追踪的 GDTM 数据集，该数据集包含九小时的数据，可以用于优化多模态数据处理架构、研究模型对不良传感条件和传感器放置变化的鲁棒性。