论文标题
通过学习一致性字段朝着高效的神经场景图
Towards Efficient Neural Scene Graphs by Learning Consistency Fields
论文作者
论文摘要
神经辐射场（NERF）从新视图中实现了光真实的图像，而神经场景图（NSG）\ cite {ost2021neural}将其扩展到带有多个对象的动态场景（视频）。然而，每个图像框架的计算重射线都会成为巨大的负担。在本文中，利用视频中相邻帧的大量冗余，我们提出了一个固定功能的框架。但是，从天真地重复使用NSG功能的第一次尝试中，我们了解到将跨帧与瞬态框架保持一致的对象内在属性至关重要。我们提出的方法，\ textIt {基于一致性的NSG（CF-NSG）}，重新定义了神经辐射字段，以考虑\ textit {一致性字段}。 CF-NSG有了分解的表示，充分利用了功能修复方案，并以更可控制的方式执行了延长的场景操作。我们从经验上验证，CF-NSG通过使用85％的查询比NSG少的查询大大提高了推理效率，而不会显着降级。代码将提供：https：//github.com/ldynx/cf-nsg
Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video) with multiple objects. Nevertheless, computationally heavy ray marching for every image frame becomes a huge burden. In this paper, taking advantage of significant redundancy across adjacent frames in videos, we propose a feature-reusing framework. From the first try of naively reusing the NSG features, however, we learn that it is crucial to disentangle object-intrinsic properties consistent across frames from transient ones. Our proposed method, \textit{Consistency-Field-based NSG (CF-NSG)}, reformulates neural radiance fields to additionally consider \textit{consistency fields}. With disentangled representations, CF-NSG takes full advantage of the feature-reusing scheme and performs an extended degree of scene manipulation in a more controllable manner. We empirically verify that CF-NSG greatly improves the inference efficiency by using 85\% less queries than NSG without notable degradation in rendering quality. Code will be available at: https://github.com/ldynx/CF-NSG
