Learning to Approximate Directional Fields Defined over 2D Planes
Skolkovo Institute of Science and Technology
Analysis of Images, Social networks and Texts 2019
Examples of rasterized vector primitives with accompanying discretized ground-truth 2-PolyVector field derived according to our scheme
Abstract
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. A common approach to the construction of directional fields from data relies on complex optimization procedures, which are usually poorly formalizeable, require a considerable computational effort, and do not transfer across applications. In this work, we propose a deep learning-based approach and study the expressive power and generalization ability.Materials
Contact
If you have any questions about this work, please contact us under adase-3ddl@skoltech.ru.