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Oleg Voynov

7 accepted papers

2026

CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models

Vladislav Pyatov, Gleb Bobrovskikh, Saveliy Galochkin, Nikita Boldyrev, Oleg Voynov, Alexander Filippov +3
CVPR 2026

We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD systems are restricted to sketch-extrude operations due to simplified representations and limited datasets. We address this by introducing a Featur

Cited by 3Source ↗code ↗Scholar ↗
2025

A3D: Does Diffusion Dream about 3D Alignment?

Savva Victorovich Ignatyev, Nina Konovalova, Daniil Selikhanovych, Oleg Voynov, Nikolay Patakin, Ilya Olkov +6
ICLR 2025poster

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 corresponding parts aligned across them. Recent methods based on Score Distillation have succeeded in distilling the kno…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects

Yue Fan, Ningjing Fan, Ivan Skorokhodov, Oleg Voynov, Savva Ignatyev, Evgeny Burnaev +2
CVPR 2025poster

We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It is a progressive inverse rendering approach, which consists…

Cited by 17Source ↗PDF ↗Scholar ↗
2023

Multi-Sensor Large-Scale Dataset for Multi-View 3D Reconstruction

Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Saveliy Galochkin, Andrei-Timotei Ardelean, Arseniy Bozhenko +10
CVPR 2023poster

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: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and structured-light scanner. The scenes are selected to…

Cited by 12Source ↗PDF ↗Scholar ↗
2020

Deep Vectorization of Technical Drawings

Vage Egiazarian, Oleg Voynov, Alexey Artemov, Denis Volkhonskiy, Aleksandr Safin, Maria Taktasheva +2
ECCV 2020poster

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 cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-b…

Cited by 79Source ↗PDF ↗code ↗Scholar ↗
2019

Perceptual Deep Depth Super-Resolution

Oleg Voynov, Alexey Artemov, Vage Egiazarian, Alexander Notchenko, Gleb Bobrovskikh, Evgeny Burnaev +1
ICCV 2019poster

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 of depth maps by taking advantage of color information; deep learning methods make combining color and depth information…

Cited by 53PDF ↗code ↗Scholar ↗