Event camera, a bio-inspired asynchronous triggered camera, offers promising
prospects for fusion with frame-based cameras owing to its low latency and high
dynamic range. However, calibrating stereo vision systems that incorporate both
event and frame-based cameras remains a significant challenge. In this letter,
we present EF-Calib, a spatiotemporal calibration framework for event- and
frame-based cameras using continuous-time trajectories. A novel calibration
pattern applicable to both camera types and the corresponding event recognition
algorithm is proposed. Leveraging the asynchronous nature of events, a
derivable piece-wise B-spline to represent camera pose continuously is
introduced, enabling calibration for intrinsic parameters, extrinsic
parameters, and time offset, with analytical Jacobians provided. Various
experiments are carried out to evaluate the calibration performance of
EF-Calib, including calibration experiments for intrinsic parameters, extrinsic
parameters, and time offset. Experimental results show that EF-Calib achieves
the most accurate intrinsic parameters compared to current SOTA, the close
accuracy of the extrinsic parameters compared to the frame-based results, and
accurate time offset estimation. EF-Calib provides a convenient and accurate
toolbox for calibrating the system that fuses events and frames. The code of
this paper will also be open-sourced at: this https URL

提出了一个名为 EF-Calib 的时空校准框架，用于事件相机和基于帧的相机的校准，通过引入可连续表示相机姿态的 B 样条曲线模型以及分析性的雅可比矩阵，实现了内参、外参和时间偏移的校准，具有较高的准确性和方便性。

EF-Calib: 使用连续时间轨迹对事件和帧分相机进行时空标定

EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras  Using Continuous-Time Trajectories

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。