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

翻译：直接体绘制旨在帮助用户在体数据中识别和检查感兴趣区域（ROI），而支持有效ROI分类与聚类的特征表示在体探索中起着基础性作用。现有方法通常依赖显式局部特征表示或从原始体数据中学习的隐式卷积特征表示。然而，显式局部特征表示在捕捉更广泛的几何模式与空间相关性方面存在局限，而隐式卷积特征表示在实践中（尤其在用户监督有限的情况下）不一定能保证鲁棒性能。与此同时，隐式神经表示（INR）因其能够紧凑参数化连续体场，近期在体压缩领域的直接体绘制中展现出显著潜力。本文提出NeuVolEx——一种将INR的作用从体压缩扩展至更广范围的神经体探索方法。与聚焦于INR输出的现有压缩方法不同，NeuVolEx利用INR训练过程中习得的特征表示作为体探索的鲁棒基础。为更好地适配探索任务，我们在基础INR中引入结构编码器与多任务学习机制，以增强ROI表征的空间连贯性。我们在两个基础体探索任务上验证了NeuVolEx：基于图像的传递函数（TF）设计与视点推荐。对于基于图像的传递函数设计，NeuVolEx能在稀疏用户监督下实现精准ROI分类；对于视点推荐，它支持无监督聚类以识别可揭示不同ROI聚类的紧凑互补视点。在涵盖不同模态与ROI复杂度的多样化体数据集上的实验表明，NeuVolEx在有效性与可用性上均优于现有方法。