Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Volume visualization directly rendered from functional approximation generates high-quality rendering results without high-order artifacts caused by trilinear interpolations. However, querying an encoded functional approximation is computationally expensive, especially when the input dataset is large, making functional approximation impractical for interactive visualization. In this paper, we proposed a novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query. We also design a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness. Our method can not only dramatically decrease the caching time, one of the main contributors to input latency, but also effectively improve the cache hit rate through prefetching. Our approach significantly outperforms the traditional function approximation method in terms of input latency while maintaining comparable rendering quality.

翻译：函数逼近作为一种高阶连续表示，相比传统离散体数据表示能提供更精确的值与梯度查询。直接从函数逼近渲染的体可视化可生成高质量渲染结果，而无三线性插值引起的高阶伪影。然而，查询编码后的函数逼近计算成本高昂，尤其在输入数据集较大时，这使得函数逼近难以应用于交互式可视化。本文提出了一种新颖的函数逼近多分辨率表示方法——Adaptive-FAM，该方法具有轻量级特性且查询速度快。我们还设计了一个GPU加速的外核多分辨率体可视化框架，该框架直接利用Adaptive-FAM表示生成高质量渲染并保持交互响应性。我们的方法不仅能显著减少缓存时间（输入延迟的主要来源之一），还能通过预取机制有效提高缓存命中率。本方法在保持相当渲染质量的同时，在输入延迟方面显著优于传统函数逼近方法。