We introduce ENTIRE, a novel deep learning-based approach for fast and accurate volume rendering time prediction. Predicting rendering time is inherently challenging due to its dependence on multiple factors, including volume data characteristics, image resolution, camera configuration, and transfer function settings. Our method addresses this by first extracting a feature vector that encodes structural volume properties relevant to rendering performance. This feature vector is then integrated with additional rendering parameters, such as image resolution, camera setup, and transfer function settings, to produce the final prediction. We evaluate ENTIRE across multiple rendering frameworks (CPU- and GPU-based) and configurations (with and without single-scattering) on diverse datasets. The results demonstrate that our model achieves high prediction accuracy with fast inference speed and can be efficiently adapted to new scenarios by fine-tuning the pretrained model with few samples. Furthermore, we showcase ENTIRE's effectiveness in two case studies, where it enables dynamic parameter adaptation for stable frame rates and load balancing.

翻译：我们提出ENTIRE，一种基于深度学习的新型快速准确体渲染时间预测方法。由于渲染时间取决于多个因素（包括体数据特征、图像分辨率、相机配置和传递函数设置），其预测本身就具有挑战性。我们的方法通过首先提取编码与渲染性能相关的体结构属性的特征向量来解决这一问题。然后，该特征向量与额外的渲染参数（如图像分辨率、相机设置和传递函数设置）进行整合，以生成最终预测。我们在多种渲染框架（基于CPU和GPU）和配置（有无单次散射）上，使用不同数据集对ENTIRE进行评估。结果表明，我们的模型在实现高预测精度的同时具备快速推理速度，并能通过少量样本微调预训练模型高效适应新场景。此外，我们在两个案例研究中展示了ENTIRE的有效性：它能够实现稳定帧率的动态参数自适应和负载均衡。