# Learning to Approximate Directional Fields Defined over 2D Planes

## TL;DR

This paper introduces a deep learning method for reconstructing directional fields over 2D planes, aiming to improve efficiency and generalization in geometry processing tasks.

## Contribution

It presents a novel deep learning approach for approximating directional fields, addressing limitations of traditional optimization-based methods.

## Key findings

- The method demonstrates strong generalization across different geometry processing tasks.
- It reduces computational complexity compared to traditional optimization techniques.
- The approach shows promising results in reconstructing directional fields from data.

## 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 formalizable, 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.