Ruslan Rakhimov

I am a Lead Research Engineer at the Robotics Center and hold a Ph.D. in Computer Science, which I earned at Skoltech within Evgeny Burnaev's Applied AI Center.

I received a Master's Degree in Data Science from the Skoltech in 2020. Prior to that, I received my Bachelor's in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology (MIPT) in 2018.

Email / Google Scholar / Twitter / Github

News

Sep. 2023: Joined Sber Robotics Center
Aug. 2023: Our work on reconstruction of 3D meshes of human heads from one view has been accepted to IEEE Access
July 2023: Delivered three lectures on Introduction to 3D Computer Vision at the AIRI Summer School
Nov. 2022: Became a recipient of The Ilya Segalovich Scientific Award for Young Researchers from Yandex

Work Experience

Sber Robotics Center: Lead Research Engineer (September 2023 - Present)
Applied AI Center: Research Engineer (November 2019 - August 2023)
Huawei: Research Intern (Summer 2019)

Research

I'm interested in 3d computer vision, deep learning, robotics.

	T-3DGS: Removing Transient Objects for 3D Scene Reconstruction Vadim Pryadilshchikov, Alexander Markin, Artem Komarichev, Ruslan Rakhimov, Peter Wonka, Evgeny Burnaev Preprint, 2024 project page / arXiv / code We propose a novel framework that leverages 3D Gaussian Splatting to effectively remove transient objects from input videos, enabling accurate and stable 3D scene reconstruction.
	GSplatLoc: Grounding Keypoint Descriptors into 3D Gaussian Splatting for Improved Visual Localization Gennady Sidorov, Malik Mohrat, Ksenia Lebedeva, Ruslan Rakhimov, Serkey Kolubin Preprint, 2024 project page / arXiv / code We present a novel approach that integrates dense keypoint descriptors into 3D Gaussian Splatting to enhance visual localization, achieving state-of-the-art performance on popular indoor and outdoor benchmarks.
	NPBG++: Accelerating Neural Point-Based Graphics Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, Evgeny Burnaev CVPR, 2022 project page / arXiv / code We take the original NPBG pipeline and make it work without per-scene optimization.
	DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes Albert Matveev, Ruslan Rakhimov, Alexey Artemov, Gleb Bobrovskikh, Vage Egiazarian, Emil Bogomolov, Daniele Panozzo, Denis Zorin, Evgeny Burnaev SIGGRAPH, 2022 project page / arXiv code Differently from existing data-driven methods for predicting sharp geometric features in sampled 3D shapes, which reduce this problem to feature classification, we propose to regress a scalar field representing the distance from point samples to the closest feature line on local patches.
	Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions Egor Burkov, Ruslan Rakhimov, Aleksandr Safin, Evgeny Burnaev, Victor Lempitsky IEEE Access project page / arXiv We present an approach for the reconstruction of textured 3D meshes of human heads from one or few views.
	Multi-sensor large-scale dataset for multi-view 3D reconstruction Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Andrei-Timotei Ardelean, Arseniy Bozhenko, Saveliy Galochkin, Ekaterina Karmanova, Pavel Kopanev, Yaroslav Labutin-Rymsho, Ruslan Rakhimov, Aleksandr Safin, Valerii Serpiva, Alexey Artemov, Evgeny Burnaev, Dzmitry Tsetserukou, Denis Zorin CVPR, 2023 project page / arXiv A new multi-sensor dataset for 3D surface reconstruction that includes registered RGB and depth data from sensors of different resolutions and modalities under a large number of lighting conditions.
	Making DensePose fast and light Ruslan Rakhimov, Emil Bogomolov, Alexandr Notchenko, Fung Mao, Alexey Artemov, Denis Zorin, Evgeny Burnaev WACV, 2021 arXiv / code We target the problem of redesigning the DensePose R-CNN model's architecture so that the final network retains most of its accuracy but becomes more light-weight and fast.
	Latent Video Transformer Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, Evgeny Burnaev VISIGRAPP, 2021 arXiv / code We predict future video frames in latent space in an autoregressive manner.

Thanks to Jon Barron for the website template.