Volume visualization plays a crucial role in both academia and industry, as volumetric data is extensively utilized in fields such as medicine, geosciences, and engineering. Addressing the complexities of volume rendering, neural rendering has emerged as a potential solution, facilitating the production of high-quality volume rendered images. In this paper, we propose TwineNet, a neural network architecture specifically designed for volume rendering. TwineNet combines features extracted from volume data, transfer functions, and viewpoints by utilizing twining skip connections across multiple feature layers. Building upon the TwineNet architecture, we introduce two neural networks, VolTFNet and PosTFNet, which leverage convolutional encoder–decoders and multilayer perceptrons to synthesize volume rendered images with novel transfer functions and viewpoints. Our experimental findings demonstrate the superiority of our models compared to state-of-the-art approaches in generating high-quality volume rendered images with novel transfer functions and viewpoints. This research contributes to advancing the field of volume rendering and showcases the potential of neural rendering techniques in scientific visualization.

体积可视化在学术界和工业界都发挥着至关重要的作用，因为体积数据广泛应用于医学、地球科学和工程等领域。为了解决体积渲染的复杂性，神经渲染已成为一种潜在的解决方案，有助于生成高质量的体积渲染图像。在本文中，我们提出了 TwineNet，一种专门为体渲染设计的神经网络架构。 TwineNet 通过利用跨多个特征层的缠绕跳跃连接来组合从体数据、传递函数和视点中提取的特征。在 TwineNet 架构的基础上，我们引入了两个神经网络 VolTFNet 和 PosTFNet，它们利用卷积编码器解码器和多层感知器来合成具有新颖传递函数和视点的体积渲染图像。我们的实验结果证明，与最先进的方法相比，我们的模型在生成具有新颖的传递函数和视点的高质量体积渲染图像方面具有优越性。这项研究有助于推进体积渲染领域，并展示了神经渲染技术在科学可视化中的潜力。