Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Demonceaux, Cedric"'
This paper presents a dense depth estimation approach from light-field (LF) images that is able to compensate for strong rolling shutter (RS) effects. Our method estimates RS compensated views and dense RS compensated disparity maps. We present a two
Externí odkaz:
http://arxiv.org/abs/2412.03518
Autor:
Cadar, Felipe, Potje, Guilherme, Martins, Renato, Demonceaux, Cédric, Nascimento, Erickson R.
Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques for matching keypoints currently involve using learned sparse or dense m
Externí odkaz:
http://arxiv.org/abs/2410.09533
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
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
In this paper, we propose an approach to address the problem of 3D reconstruction of scenes from a single image captured by a light-field camera equipped with a rolling shutter sensor. Our method leverages the 3D information cues present in the light
Externí odkaz:
http://arxiv.org/abs/2311.01292
Autor:
Tel, Steven, Wu, Zongwei, Zhang, Yulun, Heyrman, Barthélémy, Demonceaux, Cédric, Timofte, Radu, Ginhac, Dominique
High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual information, including both dynamic and static semantics, for better image ge
Externí odkaz:
http://arxiv.org/abs/2305.18135
Autor:
Wu, Zongwei, Wang, Jingjing, Zhou, Zhuyun, An, Zhaochong, Jiang, Qiuping, Demonceaux, Cédric, Sun, Guolei, Timofte, Radu
Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In this paper, we propose a novel approach by mining the Cross-Modal S
Externí odkaz:
http://arxiv.org/abs/2305.10469
Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in
Externí odkaz:
http://arxiv.org/abs/2303.07182
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
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues.
Externí odkaz:
http://arxiv.org/abs/2301.07405