no code implementations • CVPR 2014 • Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars
We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class.
no code implementations • CVPR 2015 • Konstantinos Rematas, Basura Fernando, Frank Dellaert, Tinne Tuytelaars
As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content.
no code implementations • CVPR 2016 • Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Efstratios Gavves, Tinne Tuytelaars
Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constraint nature of this inverse problem.
no code implementations • 31 Jan 2016 • Konstantinos Rematas, Chuong Nguyen, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars
We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape.
no code implementations • 27 Mar 2016 • Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Luc van Gool, Tinne Tuytelaars
In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i. e. from a single 2D image of a sphere of one material under one illumination.
no code implementations • ICCV 2017 • Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Mario Fritz, Tinne Tuytelaars, Luc van Gool
How much does a single image reveal about the environment it was taken in?
no code implementations • CVPR 2018 • Konstantinos Rematas, Ira Kemelmacher-Shlizerman, Brian Curless, Steve Seitz
We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device.
1 code implementation • 26 Sep 2018 • Keunhong Park, Konstantinos Rematas, Ali Farhadi, Steven M. Seitz
Existing online 3D shape repositories contain thousands of 3D models but lack photorealistic appearance.
1 code implementation • CVPR 2020 • Konstantinos Rematas, Vittorio Ferrari
Finally, we show how our neural rendering framework can capture and faithfully render objects from real images and from a diverse set of classes.
2 code implementations • ECCV 2020 • Luyang Zhu, Konstantinos Rematas, Brian Curless, Steve Seitz, Ira Kemelmacher-Shlizerman
Based on these models, we introduce a new method that takes as input a single photo of a clothed player in any basketball pose and outputs a high resolution mesh and 3D pose for that player.
no code implementations • CVPR 2021 • Francis Engelmann, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari
We propose a method to detect and reconstruct multiple 3D objects from a single RGB image.
no code implementations • 17 Feb 2021 • Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari
We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.
no code implementations • CVPR 2022 • Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan, Jonathan T. Barron, Andrea Tagliasacchi, Thomas Funkhouser, Vittorio Ferrari
The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e. g., Street View).
no code implementations • 21 Nov 2023 • Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas, Krishna Murthy Jatavallabhula, Cengiz Oztireli, Tzu-Mao Li, Derek Nowrouzezahrai
This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.