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.

提议一种将光场网络以连续的细节层次进行编码的方法，通过使用累积区域表滤波和基于显著性的重要性采样，使神经表示逐渐流动，减少渲染的延迟和资源利用。

光场网络的连续细节层次

Continuous Levels of Detail for Light Field Networks

Light Field Networks, the re-formulations of radiance fields to oriented
rays, are magnitudes faster than their coordinate network counterparts, and
provide higher fidelity with respect to representing 3D structures from 2D
observations. They would be well suited for generic scene representation and
manipulation, but suffer from one problem: they are limited to holistic and
static scenes. In this paper, we propose the Dynamic Light Field Network
(DyLiN) method that can handle non-rigid deformations, including topological
changes. We learn a deformation field from input rays to canonical rays, and
lift them into a higher dimensional space to handle discontinuities. We further
introduce CoDyLiN, which augments DyLiN with controllable attribute inputs. We
train both models via knowledge distillation from pretrained dynamic radiance
fields. We evaluated DyLiN using both synthetic and real world datasets that
include various non-rigid deformations. DyLiN qualitatively outperformed and
quantitatively matched state-of-the-art methods in terms of visual fidelity,
while being 25 - 71x computationally faster. We also tested CoDyLiN on
attribute annotated data and it surpassed its teacher model. Project page:
this https URL .

本文提出了动态光场网络（DyLiN）和可控动态光场网络（CoDyLiN）方法，其中动态光场网络可以处理包括拓扑变化在内的非刚性变形。我们通过预先训练的动态辐射场进行知识蒸馏，评估表明 DyLiN 在视觉保真度方面优于最先进的方法，并且在计算速度上快 25-71 倍。CoDyLiN 在属性注释数据上表现优异。

DyLiN: 让光场网络变得动态

DyLiN: Making Light Field Networks Dynamic

Inferring representations of 3D scenes from 2D observations is a fundamental
problem of computer graphics, computer vision, and artificial intelligence.
Emerging 3D-structured neural scene representations are a promising approach to
3D scene understanding. In this work, we propose a novel neural scene
representation, Light Field Networks or LFNs, which represent both geometry and
appearance of the underlying 3D scene in a 360-degree, four-dimensional light
field parameterized via a neural implicit representation. Rendering a ray from
an LFN requires only a single network evaluation, as opposed to hundreds of
evaluations per ray for ray-marching or volumetric based renderers in
3D-structured neural scene representations. In the setting of simple scenes, we
leverage meta-learning to learn a prior over LFNs that enables multi-view
consistent light field reconstruction from as little as a single image
observation. This results in dramatic reductions in time and memory complexity,
and enables real-time rendering. The cost of storing a 360-degree light field
via an LFN is two orders of magnitude lower than conventional methods such as
the Lumigraph. Utilizing the analytical differentiability of neural implicit
representations and a novel parameterization of light space, we further
demonstrate the extraction of sparse depth maps from LFNs.

本文提出了一种新型的神经场景表示法，Light Field Networks (LFNs)，其可实现基于单一神经网络评估的 360 度四维光场参数化的几何和外观，通过元学习优化 LFNs 的先验使其能够从单个图像观察中实现多视角一致的光场重建，从而实现了实时渲染，并能够从 LFNs 中提取稀疏深度图。