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pro vyhledávání: '"Karaev, Nikita"'
Autor:
Karaev, Nikita, Makarov, Iurii, Wang, Jianyuan, Neverova, Natalia, Vedaldi, Andrea, Rupprecht, Christian
Most state-of-the-art point trackers are trained on synthetic data due to the difficulty of annotating real videos for this task. However, this can result in suboptimal performance due to the statistical gap between synthetic and real videos. In orde
Externí odkaz:
http://arxiv.org/abs/2410.11831
Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incre
Externí odkaz:
http://arxiv.org/abs/2312.04563
Autor:
Karaev, Nikita, Rocco, Ignacio, Graham, Benjamin, Neverova, Natalia, Vedaldi, Andrea, Rupprecht, Christian
We introduce CoTracker, a transformer-based model that tracks a large number of 2D points in long video sequences. Differently from most existing approaches that track points independently, CoTracker tracks them jointly, accounting for their dependen
Externí odkaz:
http://arxiv.org/abs/2307.07635
Autor:
Karaev, Nikita, Rocco, Ignacio, Graham, Benjamin, Neverova, Natalia, Vedaldi, Andrea, Rupprecht, Christian
We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consisten
Externí odkaz:
http://arxiv.org/abs/2305.02296