Search Results for author: Alexey Artemov

Found 20 papers, 11 papers with code

3D Parametric Wireframe Extraction Based on Distance Fields

no code implementations13 Jul 2021 Albert Matveev, Alexey Artemov, Denis Zorin, Evgeny Burnaev

We present a pipeline for parametric wireframe extraction from densely sampled point clouds.

Unpaired Depth Super-Resolution in the Wild

no code implementations25 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.

Depth Map Super-Resolution Image-to-Image Translation +1

Towards Part-Based Understanding of RGB-D Scans

1 code implementation CVPR 2021 Alexey Bokhovkin, Vladislav Ishimtsev, Emil Bogomolov, Denis Zorin, Alexey Artemov, Evgeny Burnaev, Angela Dai

Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-grained understanding is required to enable interactions with objects and their functional understanding.

3D Instance Segmentation Scene Understanding +1

DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes

1 code implementation30 Nov 2020 Albert Matveev, Ruslan Rakhimov, Alexey Artemov, Gleb Bobrovskikh, Vage Egiazarian, Emil Bogomolov, Daniele Panozzo, Denis Zorin, Evgeny Burnaev

We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes.

CAD-Deform: Deformable Fitting of CAD Models to 3D Scans

1 code implementation ECCV 2020 Vladislav Ishimtsev, Alexey Bokhovkin, Alexey Artemov, Savva Ignatyev, Matthias Niessner, Denis Zorin, Evgeny Burnaev

Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios.

Geometric Attention for Prediction of Differential Properties in 3D Point Clouds

no code implementations6 Jul 2020 Albert Matveev, Alexey Artemov, Denis Zorin, Evgeny Burnaev

Estimation of differential geometric quantities in discrete 3D data representations is one of the crucial steps in the geometry processing pipeline.

Surface Reconstruction

Making DensePose fast and light

1 code implementation26 Jun 2020 Ruslan Rakhimov, Emil Bogomolov, Alexandr Notchenko, Fung Mao, Alexey Artemov, Denis Zorin, Evgeny Burnaev

DensePose estimation task is a significant step forward for enhancing user experience computer vision applications ranging from augmented reality to cloth fitting.

3D Human Pose Estimation Quantization

Latent Video Transformer

1 code implementation18 Jun 2020 Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, Evgeny Burnaev

After the transformation of frames into the latent space, our model predicts latent representation for the next frames in an autoregressive manner.

Video Generation Video Prediction

Deep Vectorization of Technical Drawings

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.

Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds

1 code implementation13 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.

Generating 3D Point Clouds Representation Learning

Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

1 code implementation5 Nov 2019 Sergey Pavlov, Alexey Artemov, Maksim Sharaev, Alexander Bernstein, Evgeny Burnaev

Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain.

Brain Tumor Segmentation Tumor Segmentation

Learning to Approximate Directional Fields Defined over 2D Planes

no code implementations1 Jul 2019 Maria Taktasheva, Albert Matveev, Alexey Artemov, Evgeny Burnaev

Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions.

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

1 code implementation20 May 2019 Maria Kolos, Anton Marin, Alexey Artemov, Evgeny Burnaev

Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant.

Change Detection

Monocular 3D Object Detection via Geometric Reasoning on Keypoints

no code implementations14 May 2019 Ivan Barabanau, Alexey Artemov, Evgeny Burnaev, Vyacheslav Murashkin

Monocular 3D object detection is well-known to be a challenging vision task due to the loss of depth information; attempts to recover depth using separate image-only approaches lead to unstable and noisy depth estimates, harming 3D detections.

Keypoint Detection Monocular 3D Object Detection +1

Perceptual deep depth super-resolution

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.


ABC: A Big CAD Model Dataset For Geometric Deep Learning

3 code implementations CVPR 2019 Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo

We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications.

fMRI: preprocessing, classification and pattern recognition

no code implementations26 Apr 2018 Maxim Sharaev, Alexander Andreev, Alexey Artemov, Alexander Bernstein, Evgeny Burnaev, Ekaterina Kondratyeva, Svetlana Sushchinskaya, Renat Akzhigitov

As machine learning continues to gain momentum in the neuroscience community, we witness the emergence of novel applications such as diagnostics, characterization, and treatment outcome prediction for psychiatric and neurological disorders, for instance, epilepsy and depression.

Classification General Classification

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