论文标题
群众群体功能
Crowdsampling the Plenoptic Function
论文作者
论文摘要
许多受欢迎的旅游地标在众多在线公开照片中被捕获。这些照片代表了特定场景的元素函数的稀疏且非结构化的采样。在本文中，我们提出了一种新的方法，用于从这些数据中随着时间变化的照明下进行新的视图综合。我们的方法建立在最近的多平面图像（MPI）格式的基础上，用于在固定观看条件下代表本地光场。我们介绍了一种新的DEEPMPI表示，这是由对元素功能的稀疏性结构的观察所激发的，该结构允许实时综合光真逼真的观点，这些观点在空间和照明中的变化中都是连续的。我们的方法可以综合与以前的MPI方法相同的引人注目的视差和观看依赖性效应，同时沿着反射率变化和随时间照明的变化插值。我们展示了如何以无监督的方式学习这些效果的模型，从无需时间注册的照片集合收集，表明对神经渲染的最新工作有了重大改进。可以找到更多信息CrowdsMpling.io。
Many popular tourist landmarks are captured in a multitude of online, public photos. These photos represent a sparse and unstructured sampling of the plenoptic function for a particular scene. In this paper,we present a new approach to novel view synthesis under time-varying illumination from such data. Our approach builds on the recent multi-plane image (MPI) format for representing local light fields under fixed viewing conditions. We introduce a new DeepMPI representation, motivated by observations on the sparsity structure of the plenoptic function, that allows for real-time synthesis of photorealistic views that are continuous in both space and across changes in lighting. Our method can synthesize the same compelling parallax and view-dependent effects as previous MPI methods, while simultaneously interpolating along changes in reflectance and illumination with time. We show how to learn a model of these effects in an unsupervised way from an unstructured collection of photos without temporal registration, demonstrating significant improvements over recent work in neural rendering. More information can be found crowdsampling.io.
