17 citations · 17 across the 4 of their papers we have counts for
4 papers
Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering
Quentin Herau, Nathan Piasco, Moussab Bennehar +6
Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccuracies in sensor calibration and e…
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
Quentin Herau, Moussab Bennehar, Arthur Moreau +6
Reliable multimodal sensor fusion algorithms require accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have…
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
In rapidly-evolving domains such as autonomous driving, the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To corr…
MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the informat…