论文标题
渐进的多尺度光场网络
Progressive Multi-scale Light Field Networks
论文作者
论文摘要
与图像集表示相比，神经表示表现在表示辐射和光场的能力方面表现出了巨大的希望。但是，当前表示不适合流式传输，因为解码只能单一的详细信息进行，并且需要下载整个神经网络模型。此外，由于对神经网络进行采样而无需进行适当的过滤，因此高分辨率光场网络可以表现出闪烁和混叠。为了解决这些问题，我们提出了一个渐进的多尺度光场网络，该网络编码具有多个细节的光场。较低级别的细节使用更少的神经网络权重编码，从而可以进行渐进的流和减少渲染时间。我们的渐进多尺度光场网络通过以较低的细节级别编码较小的反陈述来解决混蛋。此外，每个像素的细节级别使我们的表示能够支持抖动的过渡和浮动渲染。
Neural representations have shown great promise in their ability to represent radiance and light fields while being very compact compared to the image set representation. However, current representations are not well suited for streaming as decoding can only be done at a single level of detail and requires downloading the entire neural network model. Furthermore, high-resolution light field networks can exhibit flickering and aliasing as neural networks are sampled without appropriate filtering. To resolve these issues, we present a progressive multi-scale light field network that encodes a light field with multiple levels of detail. Lower levels of detail are encoded using fewer neural network weights enabling progressive streaming and reducing rendering time. Our progressive multi-scale light field network addresses aliasing by encoding smaller anti-aliased representations at its lower levels of detail. Additionally, per-pixel level of detail enables our representation to support dithered transitions and foveated rendering.
