no code implementations • 30 Nov 2023 • Natalia Soboleva, Olga Gorbunova, Maria Ivanova, Evgeny Burnaev, Matthias Nießner, Denis Zorin, Alexey Artemov
We define and learn a collection of surface-based fields to (1) capture sharp geometric features in the shape with an implicit vertexwise model and (2) approximate improvements in normals alignment obtained by applying edge-flips with an edgewise model.
1 code implementation • CVPR 2023 • Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Saveliy Galochkin, Andrei-Timotei Ardelean, Arseniy Bozhenko, Ekaterina Karmanova, Pavel Kopanev, Yaroslav Labutin-Rymsho, Ruslan Rakhimov, Aleksandr Safin, Valerii Serpiva, Alexey Artemov, Evgeny Burnaev, Dzmitry Tsetserukou, Denis Zorin
We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks.
no code implementations • CVPR 2022 • Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, Or Litany
We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression.
1 code implementation • 9 Aug 2021 • Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications.
no code implementations • 13 Jul 2021 • Albert Matveev, Alexey Artemov, Denis Zorin, Evgeny Burnaev
We present a pipeline for parametric wireframe extraction from densely sampled point clouds.
1 code implementation • 25 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.
1 code implementation • 4 May 2021 • Gal Metzer, Rana Hanocka, Denis Zorin, Raja Giryes, Daniele Panozzo, Daniel Cohen-Or
In the global phase, we propagate the orientation across all coherent patches using a dipole propagation.
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.
1 code implementation • 30 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.
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.
no code implementations • 6 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.
1 code implementation • 26 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.
1 code implementation • CVPR 2021 • Francis Williams, Matthew Trager, Joan Bruna, Denis Zorin
We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks.
1 code implementation • 18 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.
Ranked #14 on
Video Generation
on BAIR Robot Pushing
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.
2 code implementations • 9 Aug 2019 • Yixin Hu, Teseo Schneider, Bolun Wang, Denis Zorin, Daniele Panozzo
Our method builds on the TetWild algorithm, replacing the rational triangle insertion with a new incremental approach to construct and optimize the output mesh, interleaving triangle insertion and mesh optimization.
Graphics
no code implementations • NeurIPS 2019 • Francis Williams, Matthew Trager, Claudio Silva, Daniele Panozzo, Denis Zorin, Joan Bruna
We show that the gradient dynamics of such networks are determined by the gradient flow in a non-redundant parameterization of the network function.
1 code implementation • 22 Mar 2019 • Teseo Schneider, Yixin Hu, Xifeng Gao, Jeremie Dumas, Denis Zorin, Daniele Panozzo
The Finite Element Method (FEM) is widely used to solve discrete Partial Differential Equations (PDEs) in engineering and graphics applications.
Numerical Analysis
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.
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.
1 code implementation • CVPR 2019 • Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, Daniele Panozzo
We propose the use of a deep neural network as a geometric prior for surface reconstruction.
1 code implementation • 9 Apr 2018 • Teseo Schneider, Jeremie Dumas, Xifeng Gao, Mario Botsch, Daniele Panozzo, Denis Zorin
We introduce an integrated meshing and finite element method pipeline enabling black-box solution of partial differential equations in the volume enclosed by a boundary representation.
Numerical Analysis Graphics
1 code implementation • CVPR 2018 • Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna
We study data-driven representations for three-dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry.