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

We present LiDARGen, a novel, effective, and controllable generative model
that produces realistic LiDAR point cloud sensory readings. Our method
leverages the powerful score-matching energy-based model and formulates the
point cloud generation process as a stochastic denoising process in the
equirectangular view. This model allows us to sample diverse and high-quality
point cloud samples with guaranteed physical feasibility and controllability.
We validate the effectiveness of our method on the challenging KITTI-360 and
NuScenes datasets. The quantitative and qualitative results show that our
approach produces more realistic samples than other generative models.
Furthermore, LiDARGen can sample point clouds conditioned on inputs without
retraining. We demonstrate that our proposed generative model could be directly
used to densify LiDAR point clouds. Our code is available at:
this https URL

LiDARGen 是一种新颖的、有效的、可控制的生成模型，可以通过去噪声处理流程以及将点云生成过程作为随机去噪声过程表述，为我们提供多样化且高质量的点云样本，同时具有较强的物理可行性和可控性。实验证明，与其他生成模型相比，LiDARGen 可以生成更加逼真的样本，而且可以根据输入对点云进行条件采样，因此可以直接用于加密测距雷达 (point cloud)。