Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Firman, Michael"'
Autor:
Sayed, Mohamed, Aleotti, Filippo, Watson, Jamie, Qureshi, Zawar, Garcia-Hernando, Guillermo, Brostow, Gabriel, Vicente, Sara, Firman, Michael
Estimating depth from a sequence of posed RGB images is a fundamental computer vision task, with applications in augmented reality, path planning etc. Prior work typically makes use of previous frames in a multi view stereo framework, relying on matc
Externí odkaz:
http://arxiv.org/abs/2406.18387
Autor:
Watson, Jamie, Aleotti, Filippo, Sayed, Mohamed, Qureshi, Zawar, Mac Aodha, Oisin, Brostow, Gabriel, Firman, Michael, Vicente, Sara
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competiti
Externí odkaz:
http://arxiv.org/abs/2406.08960
Autor:
Watson, Jamie, Sayed, Mohamed, Qureshi, Zawar, Brostow, Gabriel J., Vicente, Sara, Mac Aodha, Oisin, Firman, Michael
For augmented reality (AR), it is important that virtual assets appear to `sit among' real world objects. The virtual element should variously occlude and be occluded by real matter, based on a plausible depth ordering. This occlusion should be consi
Externí odkaz:
http://arxiv.org/abs/2305.07014
Autor:
Weder, Silvan, Garcia-Hernando, Guillermo, Monszpart, Aron, Pollefeys, Marc, Brostow, Gabriel, Firman, Michael, Vicente, Sara
Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal in
Externí odkaz:
http://arxiv.org/abs/2212.11966
Autor:
Sayed, Mohamed, Gibson, John, Watson, Jamie, Prisacariu, Victor, Firman, Michael, Godard, Clément
Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction. Recently, a family of methods have emerged that perform reconstruction directly i
Externí odkaz:
http://arxiv.org/abs/2208.14743
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
Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at
Externí odkaz:
http://arxiv.org/abs/2104.14540
Our goal is to forecast the near future given a set of recent observations. We think this ability to forecast, i.e., to anticipate, is integral for the success of autonomous agents which need not only passively analyze an observation but also must re
Externí odkaz:
http://arxiv.org/abs/2104.03962