用隐式神经表示提升体数据探索，支持少标注下的兴趣区域识别
NeuVolEx: Implicit Neural Features for Volume Exploration
- 利用训练过程中的隐式神经特征，替代传统显式或卷积特征
- 在稀疏用户标注下实现高精度兴趣区域分类，无需依赖大量标注
- 适合需要高效体数据探索的医学影像、科学计算等场景
直接体渲染（DVR）旨在帮助用户识别和分析体数据中的感兴趣区域（ROIs），而支持有效ROI分类与聚类的特征表示在体数据探索中起着基础作用。现有方法通常依赖于显式局部特征或从原始体积数据中学习的隐式卷积特征。然而，显式局部特征难以捕捉更广泛的几何模式和空间相关性，而隐式卷积特征在实际应用中表现不稳定，尤其在用户监督有限的情况下。与此同时，隐式神经表示（INRs）在体数据压缩方面展现出强大潜力，因其能紧凑地参数化连续体场。本文提出NeuVolEx，一种将INRs扩展至体数据探索的新方法。不同于以往仅关注INR输出的压缩方法，NeuVolEx利用训练过程中学习到的特征表示作为探索任务的鲁棒基础。为更好地适应探索需求，我们在基础INR上引入结构编码器和多任务学习机制，提升ROI表征的空间一致性。我们在两个基本探索任务上验证了NeuVolEx：基于图像的转移函数（TF）设计与视点推荐。实验表明，NeuVolEx在稀疏用户标注下实现了准确的ROI分类，并支持无监督聚类，识别出能揭示不同ROI簇的紧凑互补视点。在多种模态、复杂度各异的体数据集上的实验表明，该方法在效果与可用性上均优于先前方法。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。