Learning-based density-equalizing map

TL;DR

This paper introduces a deep learning framework for density-equalizing maps that improves accuracy, avoids overlaps, and seamlessly extends from 2D to 3D, outperforming traditional methods in various applications.

Contribution

We propose a novel learning-based approach for density-equalizing maps that enhances accuracy, bijectivity, and scalability, with a hierarchical neural network architecture and a specialized loss function.

Findings

Superior density-equalizing and bijectivity properties compared to prior methods.

Effective application to surface remeshing with different effects.

Seamless generalization from 2D to 3D domains without structural changes.

Abstract

Density-equalizing map (DEM) serves as a powerful technique for creating shape deformations with the area changes reflecting an underlying density function. In recent decades, DEM has found widespread applications in fields such as data visualization, geometry processing, and medical imaging. Traditional approaches to DEM primarily rely on iterative numerical solvers for diffusion equations or optimization-based methods that minimize handcrafted energy functionals. However, these conventional techniques often face several challenges: they may suffer from limited accuracy, produce overlapping artifacts in extreme cases, and require substantial algorithmic redesign when extended from 2D to 3D, due to the derivative-dependent nature of their energy formulations. In this work, we propose a novel learning-based density-equalizing mapping framework (LDEM) using deep neural networks. Specifically, we introduce a loss function that enforces density uniformity and geometric regularity, and utilize a hierarchical approach to predict the transformations at both the coarse and dense levels. Our method demonstrates superior density-equalizing and bijectivity properties compared to prior methods for a wide range of simple and complex density distributions, and can be easily applied to surface remeshing with different effects. Also, it generalizes seamlessly from 2D to 3D domains without structural changes to the model architecture or loss formulation. Altogether, our work opens up new possibilities for scalable and robust computation of density-equalizing maps for practical applications.