O
Oleg Voynov
Researcher at Skolkovo Institute of Science and Technology
Publications - 9
Citations - 92
Oleg Voynov is an academic researcher from Skolkovo Institute of Science and Technology. The author has contributed to research in topics: Depth map & Upsampling. The author has an hindex of 3, co-authored 9 publications receiving 54 citations. Previous affiliations of Oleg Voynov include Moscow Institute of Physics and Technology.
Papers
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Proceedings ArticleDOI
Perceptual Deep Depth Super-Resolution
Oleg Voynov,Alexey Artemov,Vage Egiazarian,Alexandr Notchenko,Gleb Bobrovskikh,Evgeny Burnaev,Denis Zorin +6 more
TL;DR: In this article, the authors measure the quality of depth map upsampling using renderings of resulting 3D surfaces, and demonstrate that a simple visual appearance-based loss, when used with either a trained CNN or simply a deep prior, yields significantly improved 3D shapes as measured by a number of existing perceptual metrics.
Posted Content
Perceptual deep depth super-resolution
Oleg Voynov,Alexey Artemov,Vage Egiazarian,Alexander Notchenko,Gleb Bobrovskikh,Denis Zorin,Evgeny Burnaev +6 more
TL;DR: This work demonstrates that a simple visual appearance-based loss, when used with either a trained CNN or simply a deep prior, yields significantly improved 3D shapes, as measured by a number of existing perceptual metrics.
Book ChapterDOI
Deep Vectorization of Technical Drawings
Vage Egiazarian,Oleg Voynov,Alexey Artemov,Denis Volkhonskiy,Aleksandr Safin,Maria Taktasheva,Denis Zorin,Denis Zorin,Evgeny Burnaev +8 more
TL;DR: This work presents a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images, that quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings.
Book ChapterDOI
Deep Vectorization of Technical Drawings
Vage Egiazarian,Oleg Voynov,Alexey Artemov,Denis Volkhonskiy,Aleksandr Safin,Maria Taktasheva,Denis Zorin,Denis Zorin,Evgeny Burnaev +8 more
TL;DR: In this paper, a transformer-based network is used to estimate vector primitives and an optimization procedure is performed to obtain the final primitive configurations, which outperforms a number of existing techniques on a collection of representative technical drawings.
Proceedings ArticleDOI
Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds.
Vage Egiazarian,Savva Ignatyev,Alexey Artemov,Oleg Voynov,Andrey Kravchenko,Youyi Zheng,Luiz Velho,Evgeny Burnaev +7 more
TL;DR: This work combines the recently proposed latent-space GAN and Laplacian GAN architectures to form a multi-scale model capable of generating 3D point clouds at increasing levels of detail and demonstrates that this model outperforms the existing generative models for 3Dpoint clouds.