Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Tsishkou, Dzmitry"'
Autor:
Herau, Quentin, Bennehar, Moussab, Moreau, Arthur, Piasco, Nathan, Roldao, Luis, Tsishkou, Dzmitry, Migniot, Cyrille, Vasseur, Pascal, Demonceaux, Cédric
Reliable multimodal sensor fusion algorithms require accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have proven to provide precise and robust results. Nevertheless, such metho
Externí odkaz:
http://arxiv.org/abs/2403.11577
Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large computational cost of volumetric rendering. More recently, 3D Gaussian
Externí odkaz:
http://arxiv.org/abs/2403.10427
Autor:
Djeghim, Hala, Piasco, Nathan, Bennehar, Moussab, Roldão, Luis, Tsishkou, Dzmitry, Sidibé, Désiré
Neural implicit surface representation methods have recently shown impressive 3D reconstruction results. However, existing solutions struggle to reconstruct urban outdoor scenes due to their large, unbounded, and highly detailed nature. Hence, to ach
Externí odkaz:
http://arxiv.org/abs/2403.10344
The task of separating dynamic objects from static environments using NeRFs has been widely studied in recent years. However, capturing large-scale scenes still poses a challenge due to their complex geometric structures and unconstrained dynamics. W
Externí odkaz:
http://arxiv.org/abs/2403.09419
Autor:
Herau, Quentin, Piasco, Nathan, Bennehar, Moussab, Roldão, Luis, Tsishkou, Dzmitry, Migniot, Cyrille, Vasseur, Pascal, Demonceaux, Cédric
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 correctly exploit the provided information by each sensor in a single comm
Externí odkaz:
http://arxiv.org/abs/2311.15803
Autor:
Wang, Fusang, Louys, Arnaud, Piasco, Nathan, Bennehar, Moussab, Roldão, Luis, Tsishkou, Dzmitry
Neural Radiance Fields (NeRF) enable 3D scene reconstruction from 2D images and camera poses for Novel View Synthesis (NVS). Although NeRF can produce photorealistic results, it often suffers from overfitting to training views, leading to poor geomet
Externí odkaz:
http://arxiv.org/abs/2305.16914
Autor:
Moreau, Arthur, Piasco, Nathan, Bennehar, Moussab, Tsishkou, Dzmitry, Stanciulescu, Bogdan, de La Fortelle, Arnaud
Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world. In this paper, we use them as an implicit map of a given scene and propose a camera relocalization algorithm tailored for this represen
Externí odkaz:
http://arxiv.org/abs/2303.04869
Autor:
Herau, Quentin, Piasco, Nathan, Bennehar, Moussab, Roldão, Luis, Tsishkou, Dzmitry, Migniot, Cyrille, Vasseur, Pascal, Demonceaux, Cédric
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 information provided by a variety of complimentary sensors, it is necessary to
Externí odkaz:
http://arxiv.org/abs/2303.03056
Inspired by recent developments regarding the application of self-supervised learning (SSL), we devise an auxiliary task for trajectory prediction that takes advantage of map-only information such as graph connectivity with the intent of improving ma
Externí odkaz:
http://arxiv.org/abs/2210.04672
Traffic simulation has gained a lot of interest for quantitative evaluation of self driving vehicles performance. In order for a simulator to be a valuable test bench, it is required that the driving policy animating each traffic agent in the scene a
Externí odkaz:
http://arxiv.org/abs/2208.04803