184 citations · 805 across the 36 of their papers we have counts for
9 papers · 1 filter
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…