Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Rameau, Francois"'
The large abundance of perspective camera datasets facilitated the emergence of novel learning-based strategies for various tasks, such as camera localization, single image depth estimation, or view synthesis. However, panoramic or omnidirectional im
Externí odkaz:
http://arxiv.org/abs/2406.18898
With the widespread use of NeRF-based implicit 3D representation, the need for camera localization in the same representation becomes manifestly apparent. Doing so not only simplifies the localization process -- by avoiding an outside-the-NeRF-based
Externí odkaz:
http://arxiv.org/abs/2312.15242
Reducing the number of bits needed to encode the weights and activations of neural networks is highly desirable as it speeds up their training and inference time while reducing memory consumption. It is unsurprising that considerable attention has be
Externí odkaz:
http://arxiv.org/abs/2305.10947
Autor:
Zhang, Chaoning, Puspitasari, Fachrina Dewi, Zheng, Sheng, Li, Chenghao, Qiao, Yu, Kang, Taegoo, Shan, Xinru, Zhang, Chenshuang, Qin, Caiyan, Rameau, Francois, Lee, Lik-Hang, Bae, Sung-Ho, Hong, Choong Seon
Segment anything model (SAM) developed by Meta AI Research has recently attracted significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is capable of segmenting any object on a certain image. In the original SAM
Externí odkaz:
http://arxiv.org/abs/2306.06211
Autor:
Li, Chenghao, Zhang, Chaoning, Waghwase, Atish, Lee, Lik-Hang, Rameau, Francois, Yang, Yang, Bae, Sung-Ho, Hong, Choong Seon
Generative AI (AIGC, a.k.a. AI generated content) has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions and AIGC. Due to advancements in te
Externí odkaz:
http://arxiv.org/abs/2305.06131
Inferring traffic object such as lane information is of foremost importance for deployment of autonomous driving. Previous approaches focus on offline construction of HD map inferred with GPS localization, which is insufficient for globally scalable
Externí odkaz:
http://arxiv.org/abs/2301.04470
Autor:
Hold-Geoffroy, Yannick, Piché-Meunier, Dominique, Sunkavalli, Kalyan, Bazin, Jean-Charles, Rameau, François, Lalonde, Jean-François
Image editing and compositing have become ubiquitous in entertainment, from digital art to AR and VR experiences. To produce beautiful composites, the camera needs to be geometrically calibrated, which can be tedious and requires a physical calibrati
Externí odkaz:
http://arxiv.org/abs/2208.12300
Training models dedicated to semantic segmentation requires a large amount of pixel-wise annotated data. Due to their costly nature, these annotations might not be available for the task at hand. To alleviate this problem, unsupervised domain adaptat
Externí odkaz:
http://arxiv.org/abs/2206.00181
Autor:
Nasypanyi, Oleksii, Rameau, Francois
In this thesis, we propose a pioneering work on sparse keypoints tracking across images using transformer networks. While deep learning-based keypoints matching have been widely investigated using graph neural networks - and more recently transformer
Externí odkaz:
http://arxiv.org/abs/2203.12848
Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. In this paper, we advocate that jointly so
Externí odkaz:
http://arxiv.org/abs/2111.11704