no code implementations • 2 Jan 2025 • Xudong Jiang, Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys
Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes.
1 code implementation • CVPR 2024 • Fangjinhua Wang, Xudong Jiang, Silvano Galliani, Christoph Vogel, Marc Pollefeys
We propose GLACE, which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network.
1 code implementation • CVPR 2022 • Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys
We present IterMVS, a new data-driven method for high-resolution multi-view stereo.
2 code implementations • CVPR 2021 • Arda Düzçeker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, Marc Pollefeys
We propose an online multi-view depth prediction approach on posed video streams, where the scene geometry information computed in the previous time steps is propagated to the current time step in an efficient and geometrically plausible way.
1 code implementation • CVPR 2021 • Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys
We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo.
Ranked #11 on
Point Clouds
on Tanks and Temples
2 code implementations • 25 Aug 2020 • Dorin Ungureanu, Federica Bogo, Silvano Galliani, Pooja Sama, Xin Duan, Casey Meekhof, Jan Stühmer, Thomas J. Cashman, Bugra Tekin, Johannes L. Schönberger, Pawel Olszta, Marc Pollefeys
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
3 code implementations • 12 Mar 2018 • Charis Lanaras, José Bioucas-Dias, Silvano Galliani, Emmanuel Baltsavias, Konrad Schindler
The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution.
1 code implementation • 31 Jan 2018 • Timo Hackel, Mikhail Usvyatsov, Silvano Galliani, Jan D. Wegner, Konrad Schindler
While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data.
no code implementations • CVPR 2017 • Thomas Schops, Johannes L. Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, Andreas Geiger
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task.
1 code implementation • 28 Mar 2017 • Silvano Galliani, Charis Lanaras, Dimitrios Marmanis, Emmanuel Baltsavias, Konrad Schindler
We describe a novel method for blind, single-image spectral super-resolution.
1 code implementation • ICCV 2017 • Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc van Gool, Konrad Schindler
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision.
1 code implementation • 5 Dec 2016 • Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, Uwe Stilla
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.
no code implementations • CVPR 2016 • Silvano Galliani, Konrad Schindler
By training from known points in the same image, the prediction is specifically tailored to the materials and lighting conditions of the particular scene, as well as to the precise camera viewpoint.
1 code implementation • ICCV 2015 • Silvano Galliani, Katrin Lasinger, Konrad Schindler
We present a new, massively parallel method for high-quality multiview matching.
Ranked #23 on
3D Reconstruction
on DTU