no code implementations • 7 Dec 2023 • Savva Ignatyev, Daniil Selikhanovych, Oleg Voynov, Yiqun Wang, Peter Wonka, Stamatios Lefkimmiatis, Evgeny Burnaev
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured.
no code implementations • 29 May 2023 • Yue Fan, Ivan Skorokhodov, Oleg Voynov, Savva Ignatyev, Evgeny Burnaev, Peter Wonka, Yiqun Wang
We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images.
no code implementations • CVPR 2023 • Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Saveliy Galochkin, Andrei-Timotei Ardelean, Arseniy Bozhenko, Ekaterina Karmanova, Pavel Kopanev, Yaroslav Labutin-Rymsho, Ruslan Rakhimov, Aleksandr Safin, Valerii Serpiva, Alexey Artemov, Evgeny Burnaev, Dzmitry Tsetserukou, Denis Zorin
We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks.
1 code implementation • 25 May 2021 • Aleksandr Safin, Maxim Kan, Nikita Drobyshev, Oleg Voynov, Alexey Artemov, Alexander Filippov, Denis Zorin, Evgeny Burnaev
We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps.
no code implementations • 30 Nov 2020 • Oleg Voynov, Aleksandr Safin, Savva Ignatyev, Evgeny Burnaev
We study the effects of the additional input to deep multi-view stereo methods in the form of low-quality sensor depth.
1 code implementation • ECCV 2020 • Vage Egiazarian, Oleg Voynov, Alexey Artemov, Denis Volkhonskiy, Aleksandr Safin, Maria Taktasheva, Denis Zorin, Evgeny Burnaev
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images.
1 code implementation • 13 Dec 2019 • Vage Egiazarian, Savva Ignatyev, Alexey Artemov, Oleg Voynov, Andrey Kravchenko, Youyi Zheng, Luiz Velho, Evgeny Burnaev
Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design.
1 code implementation • ICCV 2019 • Oleg Voynov, Alexey Artemov, Vage Egiazarian, Alexander Notchenko, Gleb Bobrovskikh, Denis Zorin, Evgeny Burnaev
RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common.