184 citations · 805 across the 36 of their papers we have counts for
44 papers
Convergent Incremental Potential Contact
Minchen Li, Zachary Ferguson, Teseo Schneider +5
Recent advances in the simulation of frictionally contacting elastodynamics with the Incremental Potential Contact (IPC) model have enabled inversion and intersection-free simulati…
Algebraic Smooth Occluding Contours
Ryan Capouellez, Jiacheng Dai, Aaron Hertzmann +1
Computing occluding contours is a key building block of non-photorealistic rendering, but producing contours with consistent visibility has been notoriously challenging. This paper…
Metric Optimization in Penner Coordinates
Ryan Capouellez, Denis Zorin
Many parametrization and mapping-related problems in geometry processing can be viewed as metric optimization problems, i.e., computing a metric minimizing a functional and satisfy…
High-Order Incremental Potential Contact for Elastodynamic Simulation on Curved Meshes
Zachary Ferguson, Pranav Jain, Denis Zorin +2
High-order bases provide major advantages over linear ones in terms of efficiency, as they provide (for the same physical model) higher accuracy for the same running time, and reli…
Differentiable solver for time-dependent deformation problems with contact
Zizhou Huang, Davi Colli Tozoni, Arvi Gjoka +4
We introduce a general differentiable solver for time-dependent deformation problems with contact and friction. Our approach uses a finite element discretization with a high-order…
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…
A Large-Scale Benchmark for the Incompressible Navier-Stokes Equations
Zizhou Huang, Teseo Schneider, Minchen Li +3
We introduce a collection of benchmark problems in 2D and 3D (geometry description and boundary conditions), including simple cases with known analytic solution, classical experime…
Neural Fields as Learnable Kernels for 3D Reconstruction
Francis Williams, Zan Gojcic, Sameh Khamis +4
We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art results wh…
A Cross-Platform Benchmark for Interval Computation Libraries
Xuan Tang, Zachary Ferguson, Teseo Schneider +3
Interval computation is widely used to certify computations that use floating point operations to avoid pitfalls related to rounding error introduced by inaccurate operations. Desp…
Convergence Analysis of the Algorithm in "Efficient and Robust Discrete Conformal Equivalence with Boundary"
Denis Zorin
In this note we prove that the version of Newton algorithm with line search we used in [2] converges quadratically.
GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks
Yuefan Shen, Hongbo Fu, Zhongshuo Du +5
In this paper, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). Unlike previous learning-based mesh denoising…
An Extensible Benchmark Suite for Learning to Simulate Physical Systems
Karl Otness, Arvi Gjoka, Joan Bruna +4
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-dr…
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…
Orienting Point Clouds with Dipole Propagation
Gal Metzer, Rana Hanocka, Denis Zorin +3
Establishing a consistent normal orientation for point clouds is a notoriously difficult problem in geometry processing, requiring attention to both local and global shape characte…
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…
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…
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks
Francis Williams, Matthew Trager, Joan Bruna +1
We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves…
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…
A robust solver for elliptic PDEs in 3D complex geometries
Matthew J. Morse, Abtin Rahimian, Denis Zorin
We develop a boundary integral equation solver for elliptic partial differential equations on complex \threed geometries. Our method is efficient, high-order accurate and robustly…
Scalable Simulation of Realistic Volume Fraction Red Blood Cell Flows through Vascular Networks
Libin Lu, Matthew J. Morse, Abtin Rahimian +2
High-resolution blood flow simulations have potential for developing better understanding biophysical phenomena at the microscale, such as vasodilation, vasoconstriction and overal…