用深度学习预测体渲染时间，提升实时性能调控能力
ENTIRE: Learning-based Volume Rendering Time Prediction
- 提取体积数据结构特征，融合渲染参数进行预测
- 跨框架与配置下误差低于5%，推理速度达毫秒级
- 适合需要动态调参的可视化系统与实时渲染场景
我们提出ENTIRE，一种基于深度学习的快速准确体渲染时间预测方法。由于渲染时间受体数据特性、图像分辨率、相机设置和映射函数等多种因素影响，预测极具挑战性。我们的方法首先提取编码渲染性能相关结构特性的特征向量，并将其与图像分辨率、相机配置和映射函数等渲染参数结合，生成最终预测结果。我们在多种渲染框架（CPU与GPU）及配置（有无单次散射）下，对多个数据集进行了评估。结果表明，该模型在保持高速推理的同时，预测精度高，且通过少量样本微调即可高效适配新场景。此外，两个案例研究展示了其在实现稳定帧率和负载均衡方面的有效性。
原文摘要 · Abstract (English)
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.
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