Zobrazeno 1 - 10
of 161
pro vyhledávání: '"A. Turmukhambetov"'
Autor:
Brachmann, Eric, Wynn, Jamie, Chen, Shuai, Cavallari, Tommaso, Monszpart, Áron, Turmukhambetov, Daniyar, Prisacariu, Victor Adrian
We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental reconstruction: they repeat triangulation of sparse 3D points and regis
Externí odkaz:
http://arxiv.org/abs/2404.14351
Autor:
Barroso-Laguna, Axel, Brachmann, Eric, Prisacariu, Victor Adrian, Brostow, Gabriel J., Turmukhambetov, Daniyar
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a multitude of image correspondences leads to a pool of proposed hypotheses, which are then scored to find a winning model. The inlier count is generally regarded
Externí odkaz:
http://arxiv.org/abs/2306.01596
Autor:
Wynn, Jamie, Turmukhambetov, Daniyar
Under good conditions, Neural Radiance Fields (NeRFs) have shown impressive results on novel view synthesis tasks. NeRFs learn a scene's color and density fields by minimizing the photometric discrepancy between training views and differentiable rend
Externí odkaz:
http://arxiv.org/abs/2302.12231
Autor:
Arnold, Eduardo, Wynn, Jamie, Vicente, Sara, Garcia-Hernando, Guillermo, Monszpart, Áron, Prisacariu, Victor Adrian, Turmukhambetov, Daniyar, Brachmann, Eric
Can we relocalize in a scene represented by a single reference image? Standard visual relocalization requires hundreds of images and scale calibration to build a scene-specific 3D map. In contrast, we propose Map-free Relocalization, i.e., using only
Externí odkaz:
http://arxiv.org/abs/2210.05494
Autor:
Ramamonjisoa, Michaël, Firman, Michael, Watson, Jamie, Lepetit, Vincent, Turmukhambetov, Daniyar
We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is integrated in a fully differentiable encoder-decoder architecture.
Externí odkaz:
http://arxiv.org/abs/2106.02022
Many robotics applications require interest points that are highly repeatable under varying viewpoints and lighting conditions. However, this requirement is very challenging as the environment changes continuously and indefinitely, leading to appeara
Externí odkaz:
http://arxiv.org/abs/2105.03578
Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invar
Externí odkaz:
http://arxiv.org/abs/2008.09497
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks. In this work we investigate if photorealistic image stylization improves robustness of local features to not only day-night, but also
Externí odkaz:
http://arxiv.org/abs/2008.06959
Autor:
Rau, Anita, Garcia-Hernando, Guillermo, Stoyanov, Danail, Brostow, Gabriel J., Turmukhambetov, Daniyar
To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features. This expens
Externí odkaz:
http://arxiv.org/abs/2008.05785
Autor:
Watson, Jamie, Mac Aodha, Oisin, Turmukhambetov, Daniyar, Brostow, Gabriel J., Firman, Michael
Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data during training. However, collecting large quantities of accurate dense co
Externí odkaz:
http://arxiv.org/abs/2008.01484