no code implementations • 18 Mar 2024 • Quentin Herau, Moussab Bennehar, Arthur Moreau, Nathan Piasco, Luis Roldao, Dzmitry Tsishkou, Cyrille Migniot, Pascal Vasseur, Cédric Demonceaux
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.
no code implementations • 15 Mar 2024 • Hiba Dahmani, Moussab Bennehar, Nathan Piasco, Luis Roldao, Dzmitry Tsishkou
To tackle this, we extend over 3D Gaussian Splatting to handle unstructured image collections.
no code implementations • 15 Mar 2024 • Hala Djeghim, Nathan Piasco, Moussab Bennehar, Luis Roldão, Dzmitry Tsishkou, Désiré Sidibé
SCILLA's hybrid architecture models two separate implicit fields: one for the volumetric density and another for the signed distance to the surface.
no code implementations • 14 Mar 2024 • Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco, Moussab Bennehar, Dzmitry Tsishkou
The task of separating dynamic objects from static environments using NeRFs has been widely studied in recent years.
no code implementations • 27 Nov 2023 • Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Roldão, Dzmitry Tsishkou, Cyrille Migniot, Pascal Vasseur, Cédric Demonceaux
In this paper, we leverage the ability of Neural Radiance Fields (NeRF) to represent different sensors modalities in a common volumetric representation to achieve robust and accurate spatio-temporal sensor calibration.
no code implementations • 26 May 2023 • Fusang Wang, Arnaud Louys, Nathan Piasco, Moussab Bennehar, Luis Roldão, Dzmitry Tsishkou
Neural Radiance Fields (NeRF) enable 3D scene reconstruction from 2D images and camera poses for Novel View Synthesis (NVS).
no code implementations • ICCV 2023 • Arthur Moreau, Nathan Piasco, Moussab Bennehar, Dzmitry Tsishkou, Bogdan Stanciulescu, Arnaud de La Fortelle
Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world.
no code implementations • 6 Mar 2023 • Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Roldão, Dzmitry Tsishkou, Cyrille Migniot, Pascal Vasseur, Cédric Demonceaux
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise.
no code implementations • 5 May 2022 • Arthur Moreau, Thomas Gilles, Nathan Piasco, Dzmitry Tsishkou, Bogdan Stanciulescu, Arnaud de La Fortelle
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments.
no code implementations • 13 Oct 2021 • Arthur Moreau, Nathan Piasco, Dzmitry Tsishkou, Bogdan Stanciulescu, Arnaud de La Fortelle
Neural Radiance Fields (NeRF) have recently demonstrated photo-realistic results for the task of novel view synthesis.
no code implementations • 19 Mar 2021 • Arthur Moreau, Nathan Piasco, Dzmitry Tsishkou, Bogdan Stanciulescu, Arnaud de La Fortelle
In this setup, structure-based methods require a large database, and we show that our proposal is a reliable alternative, achieving 29cm median error in a 1. 9km loop in a busy urban area
Ranked #2 on Camera Localization on Oxford RobotCar Full
no code implementations • International Conference on Robotics and Automation 2019 • Nathan Piasco, Desire Sidibe, Valerie Gouet-Brunet,Cedric Demonceaux1
We propose a new approach for outdoor large scale image based localization that can deal with challenging scenarios like cross-season, cross-weather, day/night and longterm localization.