标题： 整个：基于学习的体积渲染时间预测 显示英文标题

标题： ENTIRE: Learning-based Volume Rendering Time Prediction

摘要： 我们提出ENTIRE，一种用于体积渲染时间预测的新方法。来自模拟或实验的时变体积数据通常包含数百或数千个时间步长中的复杂变形结构，除了相机配置外，这些结构对渲染性能有显著影响。我们首先从体积中提取一个特征向量，该向量捕捉与渲染时间性能相关的结构。然后我们将此特征向量与进一步的相关参数（例如相机设置）结合，并以此进行最终预测。我们在各种数据集上进行的实验表明，我们的模型能够以快速的响应率高效地实现高预测精度。我们在两个案例研究中展示了ENTIRE在实现稳定帧率和负载平衡的动态参数适应能力。 显示英文摘要

摘要： We present ENTIRE, a novel approach for volume rendering time prediction. Time-dependent volume data from simulations or experiments typically comprise complex deforming structures across hundreds or thousands of time steps, which in addition to the camera configuration has a significant impact on rendering performance. We first extract a feature vector from a volume that captures its structure that is relevant for rendering time performance. Then we combine this feature vector with further relevant parameters (e.g. camera setup), and with this perform the final prediction. Our experiments conducted on various datasets demonstrate that our model is capable of efficiently achieving high prediction accuracy with fast response rates. We showcase ENTIRE's capability of enabling dynamic parameter adaptation for stable frame rates and load balancing in two case studies.