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Aleksandr Safin
Researcher at Skolkovo Institute of Science and Technology
Publications - 10
Citations - 61
Aleksandr Safin is an academic researcher from Skolkovo Institute of Science and Technology. The author has contributed to research in topics: Computer science & Technical drawing. The author has an hindex of 2, co-authored 5 publications receiving 38 citations. Previous affiliations of Aleksandr Safin include National Research University – Higher School of Economics.
Papers
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Journal ArticleDOI
Conformal Kernel Expected Similarity for Anomaly Detection in Time-Series data
Aleksandr Safin,Evgeny Burnaev +1 more
TL;DR: In this paper, a modification of the EXPoSE algorithm for anomaly detection in time series data is proposed, which produces a probabilistic score of abnormality, based on the expected similarity as a measure of non-conformity.
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.
Journal ArticleDOI
Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions
TL;DR: NeuS is extended, a state-of-the-art neural implicit function formulation, to represent multiple objects of a class (hu-man heads in the authors' case) simultaneously, which allows to naturally learn priors about human heads from data, and is directly convertible to textured mesh.
Journal ArticleDOI
Multi-sensor large-scale dataset for multi-view 3D reconstruction
Oleg Voynov,Gleb Bobrovskikh,Pavel A. Karpyshev,Andrei Ardelean,A A Bozhenko,Saveliy Galochkin,E. S. Karmanova,Pavel Kopanev,Yaroslav Labutin-Rymsho,Ruslan Rakhimov,Aleksandr Safin,Valerii Serpiva,Alexey Artemov,Evgeny Burnaev,Dzmitry Tsetserukou,Denis Zorin +15 more
TL;DR: This work presents a new multi-sensor dataset for 3D surface reconstruction that includes registered RGB and depth data from sensors of different resolutions and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and structured-light scanner.