10 citations · 13 across the 3 of their papers we have counts for
6 papers
Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators
Albert Matveev, Sanmitra Ghosh, Aamal Hussain +2
Operator learning is a powerful paradigm for solving partial differential equations, with Fourier Neural Operators serving as a widely adopted foundation. However, FNOs face signif…
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…
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…
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…
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…