1 code implementation • CVPR 2023 • Jamie Watson, Mohamed Sayed, Zawar Qureshi, Gabriel J. Brostow, Sara Vicente, Oisin Mac Aodha, Michael Firman
We instead propose an implicit model for depth and use that to predict the occlusion mask directly.
no code implementations • CVPR 2023 • Silvan Weder, Guillermo Garcia-Hernando, Aron Monszpart, Marc Pollefeys, Gabriel Brostow, Michael Firman, Sara Vicente
We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.
1 code implementation • 31 Aug 2022 • Mohamed Sayed, John Gibson, Jamie Watson, Victor Prisacariu, Michael Firman, Clément Godard
Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction.
1 code implementation • CVPR 2021 • Michaël Ramamonjisoa, Michael Firman, Jamie Watson, Vincent Lepetit, Daniyar Turmukhambetov
We present a novel method for predicting accurate depths from monocular images with high efficiency.
1 code implementation • CVPR 2021 • Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, Michael Firman
We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available.
Monocular Depth Estimation Unsupervised Monocular Depth Estimation
no code implementations • CVPR 2021 • Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, Alexander Schwing
Following this decomposition, we introduce panoptic segmentation forecasting.
no code implementations • CVPR 2018 • Michael Firman, Neill D. F. Campbell, Lourdes Agapito, Gabriel J. Brostow
For a single input, we learn to predict a range of possible answers.
2 code implementations • ECCV 2020 • Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov, Gabriel J. Brostow, Michael Firman
We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs.
1 code implementation • CVPR 2020 • Jamie Watson, Michael Firman, Aron Monszpart, Gabriel J. Brostow
We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input.
1 code implementation • ICCV 2019 • Jamie Watson, Michael Firman, Gabriel J. Brostow, Daniyar Turmukhambetov
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data.
Ranked #2 on Monocular Depth Estimation on VA (Virtual Apartment)
14 code implementations • 4 Jun 2018 • Clément Godard, Oisin Mac Aodha, Michael Firman, Gabriel Brostow
Per-pixel ground-truth depth data is challenging to acquire at scale.
Ranked #3 on Monocular Depth Estimation on VA (Virtual Apartment)
no code implementations • CVPR 2016 • Michael Firman, Oisin Mac Aodha, Simon Julier, Gabriel J. Brostow
Building a complete 3D model of a scene, given only a single depth image, is underconstrained.
no code implementations • 4 Apr 2016 • Michael Firman
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released.