mlsanityData to 14 Oct 2026

Uncertainty-Informed Volume Visualization using Implicit Neural Representation

arXiv 2408.06018 ↗First posted 12 Aug 2024cs.GR cs.AI cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 65 references that are in the map. First posted on arXiv 12 Aug 2024.

Builds on

33 of 65 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualizationcited by 1
  2. 2025Seeing the Many: Exploring Parameter Distributions Conditioned on Features in Surrogatescited by 0
  3. 2026Error Aware Distribution Prediction for Lightweight Implicit Neural Representationscited by 0