Transfer function design is crucial in volume rendering, as it directly influences the visual representation and interpretation of volumetric data. However, creating effective transfer functions that align with users' visual objectives is often challenging due to the complex parameter space and the semantic gap between transfer function values and features of interest within the volume. In this work, we propose a novel approach that leverages recent advancements in language-vision models to bridge this semantic gap. By employing a fully differentiable rendering pipeline and an image-based loss function guided by language descriptions, our method generates transfer functions that yield volume-rendered images closely matching the user's intent. We demonstrate the effectiveness of our approach in creating meaningful transfer functions from simple descriptions, empowering users to intuitively express their desired visual outcomes with minimal effort. This advancement streamlines the transfer function design process and makes volume rendering more accessible to a wider range of users.

翻译：传递函数设计在体绘制中至关重要，因为它直接影响体数据的视觉呈现与解读。然而，由于参数空间复杂，且传递函数值与体数据内感兴趣特征之间存在语义鸿沟，创建符合用户视觉目标的有效传递函数通常具有挑战性。本工作提出一种新颖方法，利用语言-视觉模型的最新进展来弥合这一语义鸿沟。通过采用完全可微分的渲染流程和由语言描述引导的基于图像的损失函数，我们的方法生成的传递函数能使体绘制图像紧密贴合用户意图。我们展示了该方法从简单描述中创建有意义传递函数的有效性，使用户能够以最少的精力直观表达其期望的视觉结果。这一进展简化了传递函数设计流程，并使体绘制技术对更广泛的用户群体而言更易于使用。