65 citations · 100 across the 18 of their papers we have counts for
27 papers
A3D: Does Diffusion Dream about 3D Alignment?
Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych +9
We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically…
PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing
Natalia Soboleva, Olga Gorbunova, Maria Ivanova +4
Surface reconstruction with preservation of geometric features is a challenging computer vision task. Despite significant progress in implicit shape reconstruction, state-of-the-ar…
MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
Yawar Siddiqui, Antonio Alliegro, Alexey Artemov +5
We introduce MeshGPT, a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes, in contrast to dense triangle meshes extracted b…
SSR-2D: Semantic 3D Scene Reconstruction from 2D Images
Junwen Huang, Alexey Artemov, Yujin Chen +3
Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene…
Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans
Alexandr Notchenko, Vladislav Ishimtsev, Alexey Artemov +3
We propose Scan2Part, a method to segment individual parts of objects in real-world, noisy indoor RGB-D scans. To this end, we vary the part hierarchies of objects in indoor scenes…
Multi-sensor large-scale dataset for multi-view 3D reconstruction
Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev +13
We present a new multi-sensor dataset for multi-view 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smar…
Can We Use Neural Regularization to Solve Depth Super-Resolution?
Milena Gazdieva, Oleg Voynov, Alexey Artemov +3
Depth maps captured with commodity sensors often require super-resolution to be used in applications. In this work we study a super-resolution approach based on a variational probl…
3D Parametric Wireframe Extraction Based on Distance Fields
Albert Matveev, Alexey Artemov, Denis Zorin +1
We present a pipeline for parametric wireframe extraction from densely sampled point clouds. Our approach processes a scalar distance field that represents proximity to the nearest…
Unpaired Depth Super-Resolution in the Wild
Aleksandr Safin, Maxim Kan, Nikita Drobyshev +5
Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven met…
Towards Part-Based Understanding of RGB-D Scans
Alexey Bokhovkin, Vladislav Ishimtsev, Emil Bogomolov +4
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-gra…
DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes
Albert Matveev, Ruslan Rakhimov, Alexey Artemov +6
We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven method…
Geometric Attention for Prediction of Differential Properties in 3D Point Clouds
Albert Matveev, Alexey Artemov, Denis Zorin +1
Estimation of differential geometric quantities in discrete 3D data representations is one of the crucial steps in the geometry processing pipeline. Specifically, estimating normal…
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
Vladislav Ishimtsev, Alexey Bokhovkin, Alexey Artemov +4
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 gam…
Making DensePose fast and light
Ruslan Rakhimov, Emil Bogomolov, Alexandr Notchenko +4
DensePose estimation task is a significant step forward for enhancing user experience computer vision applications ranging from augmented reality to cloth fitting. Existing neural…
Latent Video Transformer
Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov +2
The video generation task can be formulated as a prediction of future video frames given some past frames. Recent generative models for videos face the problem of high computationa…
Deep Vectorization of Technical Drawings
Vage Egiazarian, Oleg Voynov, Alexey Artemov +5
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based…
Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
Vage Egiazarian, Savva Ignatyev, Alexey Artemov +5
Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design. Despit…
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem
Sergey Pavlov, Alexey Artemov, Maksim Sharaev +2
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…
Learning to Approximate Directional Fields Defined over 2D Planes
Maria Taktasheva, Albert Matveev, Alexey Artemov +1
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 directio…
Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks
Maria Kolos, Anton Marin, Alexey Artemov +1
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. H…
Monocular 3D Object Detection via Geometric Reasoning on Keypoints
Ivan Barabanau, Alexey Artemov, Evgeny Burnaev +1
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 lea…
Perceptual deep depth super-resolution
Oleg Voynov, Alexey Artemov, Vage Egiazarian +4
RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common. One can significantly improve the resolution o…
ABC: A Big CAD Model Dataset For Geometric Deep Learning
Sebastian Koch, Albert Matveev, Zhongshi Jiang +6
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collectio…
fMRI: preprocessing, classification and pattern recognition
Maxim Sharaev, Alexander Andreev, Alexey Artemov +5
As machine learning continues to gain momentum in the neuroscience community, we witness the emergence of novel applications such as diagnostics, characterization, and treatment ou…