3D Parametric Wireframe Extraction Based on Distance Fields

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.

DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes

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.

Geometric Attention for Prediction of Differential Properties in 3D Point Clouds

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.

Surface Reconstruction

Learning to Approximate Directional Fields Defined over 2D Planes

no code implementations • 1 Jul 2019 • Maria Taktasheva, Albert Matveev, Alexey Artemov, Evgeny Burnaev

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 directions.

ABC: A Big CAD Model Dataset For Geometric Deep Learning

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.