NeuVolEx: Implicit Neural Features for Volume Exploration

2026-04-13

AI Summary

Existing volume exploration methods suffer from inadequate representation of regions of interest (ROIs): explicit local features struggle to capture global geometry and spatial relationships, while implicit convolutional features exhibit instability under sparse supervision. To address this, this work proposes NeuVolEx, the first approach to leverage intermediate training features from implicit neural representations (INRs) for volume exploration. By integrating a structural encoder with multi-task learning, NeuVolEx enhances both spatial consistency and discriminative power of the learned features. The method enables accurate classification under sparse supervision and supports unsupervised viewpoint recommendation. Evaluated on diverse volumetric modalities, NeuVolEx significantly outperforms existing techniques in image-guided transfer function design and complementary viewpoint recommendation, thereby improving both exploration efficacy and usability.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Direct volume rendering (DVR) aims to help users identify and examine regions of interest (ROIs) within volumetric data, and feature representations that support effective ROI classification and clustering play a fundamental role in volume exploration. Existing approaches typically rely on either explicit local feature representations or implicit convolutional feature representations learned from raw volumes. However, explicit local feature representations are limited in capturing broader geometric patterns and spatial correlations, while implicit convolutional feature representations do not necessarily ensure robust performance in practice, where user supervision is typically limited. Meanwhile, implicit neural representations (INRs) have recently shown strong promise in DVR for volume compression, owing to their ability to compactly parameterize continuous volumetric fields. In this work, we propose NeuVolEx, a neural volume exploration approach that extends the role of INRs beyond volume compression. Unlike prior compression methods that focus on INR outputs, NeuVolEx leverages feature representations learned during INR training as a robust basis for volume exploration. To better adapt these feature representations to exploration tasks, we augment a base INR with a structural encoder and a multi-task learning scheme that improve spatial coherence for ROI characterization. We validate NeuVolEx on two fundamental volume exploration tasks: image-based transfer function (TF) design and viewpoint recommendation. NeuVolEx enables accurate ROI classification under sparse user supervision for image-based TF design and supports unsupervised clustering to identify compact complementary viewpoints that reveal different ROI clusters. Experiments on diverse volume datasets with varying modalities and ROI complexities demonstrate NeuVolEx improves both effectiveness and usability over prior methods

Problem

• volume exploration
• region of interest
• feature representation
• direct volume rendering
• implicit neural representations

Innovation

• Implicit Neural Representations
• Volume Exploration
• Feature Representation
• Multi-task Learning
• Direct Volume Rendering