Uncertainty-Informed Volume Visualization using Implicit Neural Representation

TL;DR

This paper introduces uncertainty-aware implicit neural representations for volume visualization, leveraging deep ensemble and Monte Carlo Dropout techniques to estimate uncertainty and improve the robustness and trustworthiness of scientific data visualization.

Contribution

It presents a novel approach integrating uncertainty estimation into neural volume visualization, enabling more reliable analysis of scientific scalar field data.

Findings

Uncertainty-aware models produce more informative visualizations.

Incorporating uncertainty improves trustworthiness of DNN-based visualization.

The methods are effective across multiple scientific datasets.

Abstract

The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction quality, robustness, and uncertainty is crucial. These insights can enable domain scientists to make informed decisions about their data. However, DNNs inherently lack ability to estimate prediction uncertainty, necessitating new research to construct robust uncertainty-aware visualization techniques tailored for various visualization tasks. In this work, we propose uncertainty-aware implicit neural representations to model scalar field data sets effectively and comprehensively study the efficacy and benefits of estimated uncertainty information for volume visualization tasks. We evaluate the effectiveness of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout (MCDropout). These techniques enable uncertainty-informed volume visualization in scalar field data sets. Our extensive exploration across multiple data sets demonstrates that uncertainty-aware models produce informative volume visualization results. Moreover, integrating prediction uncertainty enhances the trustworthiness of our DNN model, making it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.