Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Revaud, Jerome"'
Autor:
Duisterhof, Bardienus, Zust, Lojze, Weinzaepfel, Philippe, Leroy, Vincent, Cabon, Yohann, Revaud, Jerome
Structure-from-Motion (SfM), a task aiming at jointly recovering camera poses and 3D geometry of a scene given a set of images, remains a hard problem with still many open challenges despite decades of significant progress. The traditional solution f
Externí odkaz:
http://arxiv.org/abs/2409.19152
Image Matching is a core component of all best-performing algorithms and pipelines in 3D vision. Yet despite matching being fundamentally a 3D problem, intrinsically linked to camera pose and scene geometry, it is typically treated as a 2D problem. T
Externí odkaz:
http://arxiv.org/abs/2406.09756
Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera parameters e.g. intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain, yet they are mandatory to triangulate corresponding pixel
Externí odkaz:
http://arxiv.org/abs/2312.14132
Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based. They lack the capability to generalize to new object categories at test time, hence severely hindering their practicability and scal
Externí odkaz:
http://arxiv.org/abs/2310.01897
Transformers have become the standard in state-of-the-art vision architectures, achieving impressive performance on both image-level and dense pixelwise tasks. However, training vision transformers for high-resolution pixelwise tasks has a prohibitiv
Externí odkaz:
http://arxiv.org/abs/2310.00632
Scene coordinates regression (SCR), i.e., predicting 3D coordinates for every pixel of a given image, has recently shown promising potential. However, existing methods remain mostly scene-specific or limited to small scenes and thus hardly scale to r
Externí odkaz:
http://arxiv.org/abs/2307.11702
Autor:
Weinzaepfel, Philippe, Lucas, Thomas, Leroy, Vincent, Cabon, Yohann, Arora, Vaibhav, Brégier, Romain, Csurka, Gabriela, Antsfeld, Leonid, Chidlovskii, Boris, Revaud, Jérôme
Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow. The application of self-supervised concepts, su
Externí odkaz:
http://arxiv.org/abs/2211.10408
Autor:
Weinzaepfel, Philippe, Leroy, Vincent, Lucas, Thomas, Brégier, Romain, Cabon, Yohann, Arora, Vaibhav, Antsfeld, Leonid, Chidlovskii, Boris, Csurka, Gabriela, Revaud, Jérôme
Masked Image Modeling (MIM) has recently been established as a potent pre-training paradigm. A pretext task is constructed by masking patches in an input image, and this masked content is then predicted by a neural network using visible patches as so
Externí odkaz:
http://arxiv.org/abs/2210.10716
Class imbalance and noisy labels are the norm rather than the exception in many large-scale classification datasets. Nevertheless, most works in machine learning typically assume balanced and clean data. There have been some recent attempts to tackle
Externí odkaz:
http://arxiv.org/abs/2108.11096
Autor:
Humenberger, Martin, Cabon, Yohann, Guerin, Nicolas, Morat, Julien, Leroy, Vincent, Revaud, Jérôme, Rerole, Philippe, Pion, Noé, de Souza, Cesar, Csurka, Gabriela
Visual localization tackles the challenge of estimating the camera pose from images by using correspondence analysis between query images and a map. This task is computation and data intensive which poses challenges on thorough evaluation of methods
Externí odkaz:
http://arxiv.org/abs/2007.13867