Recently, several approaches have emerged for generating neural representations with multiple levels of detail (LODs). LODs can improve the rendering by using lower resolutions and smaller model sizes when appropriate. However, existing methods generally focus on a few discrete LODs which suffer from aliasing and flicker artifacts as details are changed and limit their granularity for adapting to resource limitations. In this paper, we propose a method to encode light field networks with continuous LODs, allowing for finely tuned adaptations to rendering conditions. Our training procedure uses summed-area table filtering allowing efficient and continuous filtering at various LODs. Furthermore, we use saliency-based importance sampling which enables our light field networks to distribute their capacity, particularly limited at lower LODs, towards representing the details viewers are most likely to focus on. Incorporating continuous LODs into neural representations enables progressive streaming of neural representations, decreasing the latency and resource utilization for rendering.

翻译：近期，多种方法被提出用于生成具备多级细节（LODs）的神经表征。LODs可在适当情况下通过降低分辨率和模型尺寸来优化渲染效果。然而，现有方法通常专注于少数离散的LODs，这会导致细节变化时出现锯齿和闪烁伪影，并限制了其适应资源限制的粒度。本文提出一种编码具有连续LODs的光场网络的方法，能够精细调节渲染条件适应性。我们的训练过程采用求和面积表滤波，可在不同LODs下实现高效且连续的滤波。此外，我们利用基于显著性的重要性采样，使光场网络能够将其能力（尤其在低LODs下有限）集中于表达观看者最可能关注的细节。将连续LODs融入神经表征可支持渐进式神经表征流传输，从而降低渲染的延迟和资源消耗。