Fast and easy handheld capture with guideline: closest object moves at most D pixels between views Promote sampled views to local light field via layered scene representation Blend neighboring local light fields to render novel views

We explore the problem of view synthesis from a narrow baseline pair of images, and focus on generating highquality view extrapolations with plausible disocclusions. Our method builds upon prior work in predicting a multiplane image (MPI), which represents scene content as a set of RGBα planes within a reference view frustum and renders novel views by projecting this content into the target viewpoints. We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to 4× the lateral viewpoint movement allowed by prior work. Our method ameliorates two specific issues that limit the range of views renderable by prior methods: 1) We expand the range of novel views that can be rendered without depth discretization artifacts by using a 3D convolutional network architecture along with a randomized-resolution training procedure to allow our model to predict MPIs with increased disparity sampling frequency. 2) We reduce the repeated texture artifacts seen in disocclusions by enforcing a constraint that the appearance of hidden content at any depth must be drawn from visible content at or behind that depth.

We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (θ, φ)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons.

虚拟现实（VR）耳机提供了一种身临其境的立体视觉体验，但以阻止用户直接观察其物理环境的代价。传递技术旨在通过利用向外的摄像头来重建否则没有耳机的用户可以看到的图像来解决此限制。这本质上是一个实时视图综合挑战，因为传递摄像机不能与眼睛进行物理共同。现有的通行技术会遭受分散重建工件的注意力，这主要是由于缺乏准确的深度信息（尤其是对于近场和分离的物体），并且表现出有限的图像质量（例如，低分辨率和单色）。在本文中，我们提出了第一种学习的传递方法，并使用包含立体声对RGB摄像机的自定义VR耳机评估其性能。通过模拟和实验，我们证明了我们所学的传递方法与最先进的方法相比提供了卓越的图像质量，同时满足了实时的，透视透视的立体视图综合的严格VR要求，从而在广泛的视野上综合用于桌面连接的耳机。

A recent strand of work in view synthesis uses deep learning to generate multiplane images-a camera-centric, layered 3D representation-given two or more input images at known viewpoints. We apply this representation to singleview view synthesis, a problem which is more challenging but has potentially much wider application. Our method learns to predict a multiplane image directly from a single image input, and we introduce scale-invariant view synthesis for supervision, enabling us to train on online video. We show this approach is applicable to several different datasets, that it additionally generates reasonable depth maps, and that it learns to fill in content behind the edges of foreground objects in background layers.Project page at https://single-view-mpi.github.io/.

We present a method for novel view synthesis from input images that are freely distributed around a scene. Our method does not rely on a regular arrangement of input views, can synthesize images for free camera movement through the scene, and works for general scenes with unconstrained geometric layouts. We calibrate the input images via SfM and erect a coarse geometric scaffold via MVS. This scaffold is used to create a proxy depth map for a novel view of the scene. Based on this depth map, a recurrent encoder-decoder network processes reprojected features from nearby views and synthesizes the new view. Our network does not need to be optimized for a given scene. After training on a dataset, it works in previously unseen environments with no finetuning or per-scene optimization. We evaluate the presented approach on challenging real-world datasets, including Tanks and Temples, where we demonstrate successful view synthesis for the first time and substantially outperform prior and concurrent work.

where the highest resolution is required, using facial performance capture as a case in point.

We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. The core of our method is a network architecture that includes a multilayer perceptron and a ray transformer that estimates radiance and volume density at continuous 5D locations (3D spatial locations and 2D viewing directions), drawing appearance information on the fly from multiple source views. By drawing on source views at render time, our method hearkens back to classic work on image-based rendering (IBR), and allows us to render high-resolution imagery. Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes. We render images using classic volume rendering, which is fully differentiable and allows us to train using only multiview posed images as supervision. Experiments show that our method outperforms recent novel view synthesis methods that also seek to generalize to novel scenes. Further, if fine-tuned on each scene, our method is competitive with state-of-the-art single-scene neural rendering methods. 1

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

新型视图综合的古典光场渲染可以准确地再现视图依赖性效果，例如反射，折射和半透明，但需要一个致密的视图采样的场景。基于几何重建的方法只需要稀疏的视图，但不能准确地模拟非兰伯语的效果。我们介绍了一个模型，它结合了强度并减轻了这两个方向的局限性。通过在光场的四维表示上操作，我们的模型学会准确表示依赖视图效果。通过在训练和推理期间强制执行几何约束，从稀疏的视图集中毫无屏蔽地学习场景几何。具体地，我们介绍了一种基于两级变压器的模型，首先沿着ePipoll线汇总特征，然后沿参考视图聚合特征以产生目标射线的颜色。我们的模型在多个前进和360 {\ DEG}数据集中优于最先进的，具有较大的差别依赖变化的场景更大的边缘。

综合照片 - 现实图像和视频是计算机图形的核心，并且是几十年的研究焦点。传统上，使用渲染算法（如光栅化或射线跟踪）生成场景的合成图像，其将几何形状和材料属性的表示为输入。统称，这些输入定义了实际场景和呈现的内容，并且被称为场景表示（其中场景由一个或多个对象组成）。示例场景表示是具有附带纹理的三角形网格（例如，由艺术家创建），点云（例如，来自深度传感器），体积网格（例如，来自CT扫描）或隐式曲面函数（例如，截短的符号距离）字段）。使用可分辨率渲染损耗的观察结果的这种场景表示的重建被称为逆图形或反向渲染。神经渲染密切相关，并将思想与经典计算机图形和机器学习中的思想相结合，以创建用于合成来自真实观察图像的图像的算法。神经渲染是朝向合成照片现实图像和视频内容的目标的跨越。近年来，我们通过数百个出版物显示了这一领域的巨大进展，这些出版物显示了将被动组件注入渲染管道的不同方式。这种最先进的神经渲染进步的报告侧重于将经典渲染原则与学习的3D场景表示结合的方法，通常现在被称为神经场景表示。这些方法的一个关键优势在于它们是通过设计的3D-一致，使诸如新颖的视点合成捕获场景的应用。除了处理静态场景的方法外，我们还涵盖了用于建模非刚性变形对象的神经场景表示...

代表具有多个半透明彩色图层的场景是实时新型视图合成的流行和成功的选择。现有方法在平面或球形的规则间隔层上推断颜色和透明度值。在这项工作中，我们介绍了一种基于多个半透明层的新视图综合方法，具有场景适应的几何形状。我们的方法在两个阶段中介绍了立体对的这些表示。第一阶段从给定的一对视图中缩小了少数数据自适应层的几何形状。第二阶段为这些层的颜色和透明度值产生了新颖的视图合成的最终表示。重要的是，两个阶段都通过可差异化的渲染器连接，并以端到端的方式训练。在实验中，我们展示了所提出的方法在使用定期间隔的层上的优势，没有适应场景几何形状。尽管在渲染过程中较快的数量次数，但我们的方法也优于基于隐式几何表示的最近提出的IBRNET系统。查看https://samsunglabs.github.io/stereolayers的结果。

我们介绍了Fadiv-Syn，一种快速深入的新型观点合成方法。相关方法通常受到它们的深度估计阶段的限制，其中不正确的深度预测可能导致大的投影误差。为避免此问题，我们将输入图像有效地将输入图像呈现为目标帧，以为一系列假定的深度平面。得到的平面扫描量（PSV）直接进入我们的网络，首先以自我监督的方式估计软PSV掩模，然后直接产生新颖的输出视图。因此，我们侧行显式深度估计。这提高了透明，反光，薄，特色场景部件上的效率和性能。 Fadiv-syn可以在大规模Realestate10K数据集上执行插值和外推任务，优于最先进的外推方法。与可比方法相比，它由于其轻量级架构而实现了实时性能。我们彻底评估消融，例如去除软掩蔽网络，从更少的示例中培训以及更高的分辨率和更强深度离散化的概括。

https://video-nerf.github.io Figure 1. Our method takes a single casually captured video as input and learns a space-time neural irradiance field. (Top) Sample frames from the input video. (Middle) Novel view images rendered from textured meshes constructed from depth maps. (Bottom) Our results rendered from the proposed space-time neural irradiance field.

在本文中，我们为复杂场景进行了高效且强大的深度学习解决方案。在我们的方法中，3D场景表示为光场，即，一组光线，每组在到达图像平面时具有相应的颜色。对于高效的新颖视图渲染，我们采用了光场的双面参数化，其中每个光线的特征在于4D参数。然后，我们将光场配向作为4D函数，即将4D坐标映射到相应的颜色值。我们训练一个深度完全连接的网络以优化这种隐式功能并记住3D场景。然后，特定于场景的模型用于综合新颖视图。与以前需要密集的视野的方法不同，需要密集的视野采样来可靠地呈现新颖的视图，我们的方法可以通过采样光线来呈现新颖的视图并直接从网络查询每种光线的颜色，从而使高质量的灯场呈现稀疏集合训练图像。网络可以可选地预测每光深度，从而使诸如自动重新焦点的应用。我们的小说视图合成结果与最先进的综合结果相当，甚至在一些具有折射和反射的具有挑战性的场景中优越。我们在保持交互式帧速率和小的内存占地面积的同时实现这一点。

我们提出了HRF-NET，这是一种基于整体辐射场的新型视图合成方法，该方法使用一组稀疏输入来呈现新视图。最近的概括视图合成方法还利用了光辉场，但渲染速度不是实时的。现有的方法可以有效地训练和呈现新颖的观点，但它们无法概括地看不到场景。我们的方法解决了用于概括视图合成的实时渲染问题，并由两个主要阶段组成：整体辐射场预测指标和基于卷积的神经渲染器。该架构不仅基于隐式神经场的一致场景几何形状，而且还可以使用单个GPU有效地呈现新视图。我们首先在DTU数据集的多个3D场景上训练HRF-NET，并且网络只能仅使用光度损耗就看不见的真实和合成数据产生合理的新视图。此外，我们的方法可以利用单个场景的密集参考图像集来产生准确的新颖视图，而无需依赖其他明确表示，并且仍然保持了预训练模型的高速渲染。实验结果表明，HRF-NET优于各种合成和真实数据集的最先进的神经渲染方法。

Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision [20,29], but their use in graphics problems has been limited ([21, 7] are notable recent exceptions). In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to traditional approaches which consist of multiple complex stages of processing, each of which require careful tuning and can fail in unexpected ways, our system is trained end-to-end. The pixels from neighboring views of a scene are presented to the network which then directly produces the pixels of the unseen view. The benefits of our approach include generality (we only require posed image sets and can easily apply our method to different domains), and high quality results on traditionally difficult scenes. We believe this is due to the end-to-end nature of our system which is able to plausibly generate pixels according to color, depth, and texture priors learnt automatically from the training data. To verify our method we show that it can convincingly reproduce known test views from nearby imagery. Additionally we show images rendered from novel viewpoints. To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.

最近，我们看到了照片真实的人类建模和渲染的神经进展取得的巨大进展。但是，将它们集成到现有的下游应用程序中的现有网络管道中仍然具有挑战性。在本文中，我们提出了一种全面的神经方法，用于从密集的多视频视频中对人类表演进行高质量重建，压缩和渲染。我们的核心直觉是用一系列高效的神经技术桥接传统的动画网格工作流程。我们首先引入一个神经表面重建器，以在几分钟内进行高质量的表面产生。它与多分辨率哈希编码的截短签名距离场（TSDF）的隐式体积渲染相结合。我们进一步提出了一个混合神经跟踪器来生成动画网格，该网格将明确的非刚性跟踪与自我监督框架中的隐式动态变形结合在一起。前者将粗糙的翘曲返回到规范空间中，而后者隐含的一个隐含物进一步预测了使用4D哈希编码的位移，如我们的重建器中。然后，我们使用获得的动画网格讨论渲染方案，从动态纹理到各种带宽设置下的Lumigraph渲染。为了在质量和带宽之间取得复杂的平衡，我们通过首先渲染6个虚拟视图来涵盖表演者，然后进行闭塞感知的神经纹理融合，提出一个分层解决方案。我们证明了我们方法在各种平台上的各种基于网格的应用程序和照片真实的自由观看体验中的功效，即，通过移动AR插入虚拟人类的表演，或通过移动AR插入真实环境，或带有VR头戴式的人才表演。

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally-stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras. Our algorithm addresses the temporal inconsistency of disocclusions by identifying the error-prone areas with a 3D mask volume, and replaces them with static background observed throughout the video. Our method enables manipulation in 3D space as opposed to simple 2D masks, We demonstrate better temporal stability than frame-by-frame static view synthesis methods, or those that use 2D masks. The resulting view synthesis videos show minimal flickering artifacts and allow for larger translational movements.