我们提出了一种可区分的渲染算法，以进行有效的新型视图合成。通过偏离基于音量的表示，支持学习点表示，我们在训练和推理方面的内存和运行时范围内改进了现有方法的数量级。该方法从均匀采样的随机点云开始，并使用基于可区分的SPLAT渲染器来发展模型以匹配一组输入图像，从而学习了每点位置和观看依赖性外观。在训练和推理中，我们的方法比NERF快300倍，质量只有边缘牺牲，而在静态场景中使用少于10 〜MB的记忆。对于动态场景，我们的方法比Stnerf训练两个数量级，并以接近互动速率渲染，同时即使在不施加任何时间固定的正则化合物的情况下保持较高的图像质量和时间连贯性。

Volumetric neural rendering methods like NeRF generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30X faster training time. Point-NeRF can be combined with other 3D reconstruction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism. The experiments on the DTU, the NeRF Synthetics , the ScanNet and the Tanks and Temples datasets demonstrate Point-NeRF can surpass the existing methods and achieve the state-of-the-art results.

We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects. Our method can render 800×800 images at more than 150 FPS, which is over 3000 times faster than conventional NeRFs. We do so without sacrificing quality while preserving the ability of NeRFs to perform free-viewpoint rendering of scenes with arbitrary geometry and view-dependent effects. Real-time performance is achieved by pre-tabulating the NeRF into a PlenOctree. In order to preserve viewdependent effects such as specularities, we factorize the appearance via closed-form spherical basis functions. Specifically, we show that it is possible to train NeRFs to predict a spherical harmonic representation of radiance, removing the viewing direction as an input to the neural network. Furthermore, we show that PlenOctrees can be directly optimized to further minimize the reconstruction loss, which leads to equal or better quality compared to competing methods. Moreover, this octree optimization step can be used to reduce the training time, as we no longer need to wait for the NeRF training to converge fully. Our real-time neural rendering approach may potentially enable new applications such as 6-DOF industrial and product visualizations, as well as next generation AR/VR systems. PlenOctrees are amenable to in-browser rendering as well; please visit the project page for the interactive online demo, as well as video and code: https://alexyu. net/plenoctrees.

我们介绍了神经点光场，它用稀疏点云上的轻场隐含地表示场景。结合可分辨率的体积渲染与学习的隐式密度表示使得可以合成用于小型场景的新颖视图的照片现实图像。作为神经体积渲染方法需要潜在的功能场景表示的浓密采样，在沿着射线穿过体积的数百个样本，它们从根本上限制在具有投影到数百个训练视图的相同对象的小场景。向神经隐式光线推广稀疏点云允许我们有效地表示每个光线的单个隐式采样操作。这些点光场作为光线方向和局部点特征邻域的函数，允许我们在没有密集的物体覆盖和视差的情况下插入光场条件训练图像。我们评估大型驾驶场景的新型视图综合的提出方法，在那里我们综合了现实的看法，即现有的隐式方法未能代表。我们验证了神经点光场可以通过显式建模场景来实现沿着先前轨迹的视频来预测沿着看不见的轨迹的视频。

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid \textit{spatial frequency collapse} and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data are publicly available at https://github.com/seanywang0408/RadianceMapping.

本文旨在减少透明辐射场的渲染时间。一些最近的作品用图像编码器配备了神经辐射字段，能够跨越场景概括，这避免了每场景优化。但是，它们的渲染过程通常很慢。主要因素是，在推断辐射场时，它们在空间中的大量点。在本文中，我们介绍了一个混合场景表示，它结合了最佳的隐式辐射场和显式深度映射，以便有效渲染。具体地，我们首先构建级联成本量，以有效地预测场景的粗糙几何形状。粗糙几何允许我们在场景表面附近的几个点来样，并显着提高渲染速度。该过程是完全可疑的，使我们能够仅从RGB图像共同学习深度预测和辐射现场网络。实验表明，该方法在DTU，真正的前瞻性和NERF合成数据集上展示了最先进的性能，而不是比以前的最可推广的辐射现场方法快至少50倍。我们还展示了我们的方法实时综合动态人类执行者的自由观点视频。代码将在https://zju3dv.github.io/enerf/处提供。

Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detailed appearance and geometry models. Recent studies have demonstrated promising results by learning scene representations that implicitly encode both geometry and appearance without 3D supervision. However, existing approaches in practice often show blurry renderings caused by the limited network capacity or the difficulty in finding accurate intersections of camera rays with the scene geometry. Synthesizing high-resolution imagery from these representations often requires time-consuming optical ray marching. In this work, we introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering. NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree to model local properties in each cell. We progressively learn the underlying voxel structures with a diffentiable ray-marching operation from only a set of posed RGB images. With the sparse voxel octree structure, rendering novel views can be accelerated by skipping the voxels containing no relevant scene content. Our method is typically over 10 times faster than the state-of-the-art (namely, NeRF (Mildenhall et al., 2020)) at inference time while achieving higher quality results. Furthermore, by utilizing an explicit sparse voxel representation, our method can easily be applied to scene editing and scene composition. We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering. Code and data are available at our website: https://github.com/facebookresearch/NSVF.

我们探索了基于神经光场表示的几种新颖观点合成的新策略。给定目标摄像头姿势，隐式神经网络将每个射线映射到其目标像素的颜色。该网络的条件是根据来自显式3D特征量的粗量渲染产生的本地射线特征。该卷是由使用3D Convnet的输入图像构建的。我们的方法在基于最先进的神经辐射场竞争方面，在合成和真实MVS数据上实现了竞争性能，同时提供了100倍的渲染速度。

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...

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。

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/

我们重新审视NPBG，这是一种流行的新型视图合成方法，引入了无处不在的点神经渲染范式。我们对具有快速视图合成的数据效率学习特别感兴趣。除前景/背景场景渲染分裂以及改善的损失外，我们还通过基于视图的网状点描述符栅格化来实现这一目标。通过仅在一个场景上训练，我们的表现就超过了在扫描仪上接受过培训的NPBG，然后进行了填充场景。我们还针对最先进的方法SVS进行了竞争性，该方法已在完整的数据集（DTU，坦克和寺庙）上进行了培训，然后进行了对现场的培训，尽管它们具有更深的神经渲染器。

最近，神经辐射场（NERF）正在彻底改变新型视图合成（NVS）的卓越性能。但是，NERF及其变体通常需要进行冗长的每场训练程序，其中将多层感知器（MLP）拟合到捕获的图像中。为了解决挑战，已经提出了体素网格表示，以显着加快训练的速度。但是，这些现有方法只能处理静态场景。如何开发有效，准确的动态视图合成方法仍然是一个开放的问题。将静态场景的方法扩展到动态场景并不简单，因为场景几何形状和外观随时间变化。在本文中，基于素素网格优化的最新进展，我们提出了一种快速变形的辐射场方法来处理动态场景。我们的方法由两个模块组成。第一个模块采用变形网格来存储3D动态功能，以及使用插值功能将观测空间中的3D点映射到规范空间的变形的轻巧MLP。第二个模块包含密度和颜色网格，以建模场景的几何形状和密度。明确对阻塞进行了建模，以进一步提高渲染质量。实验结果表明，我们的方法仅使用20分钟的训练就可以实现与D-NERF相当的性能，该训练比D-NERF快70倍以上，这清楚地证明了我们提出的方法的效率。

3D reconstruction and novel view synthesis of dynamic scenes from collections of single views recently gained increased attention. Existing work shows impressive results for synthetic setups and forward-facing real-world data, but is severely limited in the training speed and angular range for generating novel views. This paper addresses these limitations and proposes a new method for full 360{\deg} novel view synthesis of non-rigidly deforming scenes. At the core of our method are: 1) An efficient deformation module that decouples the processing of spatial and temporal information for acceleration at training and inference time; and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field. We evaluate the proposed approach on the established synthetic D-NeRF benchmark, that enables efficient reconstruction from a single monocular view per time-frame randomly sampled from a full hemisphere. We refer to this form of inputs as monocularized data. To prove its practicality for real-world scenarios, we recorded twelve challenging sequences with human actors by sampling single frames from a synchronized multi-view rig. In both cases, our method is trained significantly faster than previous methods (minutes instead of days) while achieving higher visual accuracy for generated novel views. Our source code and data is available at our project page https://graphics.tu-bs.de/publications/kappel2022fast.

新型视图合成（NVS）是一项具有挑战性的任务，需要系统从新观点中生成场景的影像图像，在新观点中，质量和速度对应用都很重要。以前的基于图像的渲染（IBR）方法很快，但是当输入视图稀疏时质量较差。最近的神经辐射场（NERF）和可推广的变体可带来令人印象深刻的结果，但不是实时的。在我们的论文中，我们提出了一种具有稀疏输入的可推广的NVS方法，称为FWD，该方法可实时提供高质量的合成。凭借明确的深度和可区分的渲染，它以130-1000 X的加速和更好的感知质量取得了SOTA方法的竞争结果。如果有的话，我们可以在训练或推理过程中无缝整合传感器深度，以提高图像质量，同时保持实时速度。随着深度传感器的越来越多的流行率，我们希望使用深度的方法将变得越来越有用。

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。

我们介绍了Plenoxels（plenoptic voxels），是一种光电型观测合成系统。Plenoxels表示作为具有球形谐波的稀疏3D网格的场景。该表示可以通过梯度方法和正则化从校准图像进行优化，而没有任何神经元件。在标准，基准任务中，Plenoxels优化了比神经辐射场更快的两个数量级，无需视觉质量损失。

Neural Radiance Field (NeRF) has revolutionized free viewpoint rendering tasks and achieved impressive results. However, the efficiency and accuracy problems hinder its wide applications. To address these issues, we propose Geometry-Aware Generalized Neural Radiance Field (GARF) with a geometry-aware dynamic sampling (GADS) strategy to perform real-time novel view rendering and unsupervised depth estimation on unseen scenes without per-scene optimization. Distinct from most existing generalized NeRFs, our framework infers the unseen scenes on both pixel-scale and geometry-scale with only a few input images. More specifically, our method learns common attributes of novel-view synthesis by an encoder-decoder structure and a point-level learnable multi-view feature fusion module which helps avoid occlusion. To preserve scene characteristics in the generalized model, we introduce an unsupervised depth estimation module to derive the coarse geometry, narrow down the ray sampling interval to proximity space of the estimated surface and sample in expectation maximum position, constituting Geometry-Aware Dynamic Sampling strategy (GADS). Moreover, we introduce a Multi-level Semantic Consistency loss (MSC) to assist more informative representation learning. Extensive experiments on indoor and outdoor datasets show that comparing with state-of-the-art generalized NeRF methods, GARF reduces samples by more than 25\%, while improving rendering quality and 3D geometry estimation.

神经表面重建旨在基于多视图图像重建准确的3D表面。基于神经量的先前方法主要训练完全隐式的模型，它们需要单个场景的数小时培训。最近的努力探讨了明确的体积表示，该表示通过记住可学习的素网格中的重要信息，从而大大加快了优化过程。但是，这些基于体素的方法通常在重建细粒几何形状方面遇到困难。通过实证研究，我们发现高质量的表面重建取决于两个关键因素：构建相干形状的能力和颜色几何依赖性的精确建模。特别是，后者是准确重建细节的关键。受这些发现的启发，我们开发了Voxurf，这是一种基于体素的方法，用于有效，准确的神经表面重建，该方法由两个阶段组成：1）利用可学习的特征网格来构建颜色场并获得连贯的粗糙形状，并且2）使用双色网络来完善详细的几何形状，可捕获精确的颜色几何依赖性。我们进一步引入了层次几何特征，以启用跨体素的信息共享。我们的实验表明，Voxurf同时达到了高效率和高质量。在DTU基准测试中，与最先进的方法相比，Voxurf获得了更高的重建质量，训练的加速度为20倍。

我们提出了一种新的方法来获取来自在线图像集合的对象表示，从具有不同摄像机，照明和背景的照片捕获任意物体的高质量几何形状和材料属性。这使得各种以各种对象渲染应用诸如新颖的综合，致密和协调的背景组合物，从疯狂的内部输入。使用多级方法延伸神经辐射场，首先推断表面几何形状并优化粗估计的初始相机参数，同时利用粗糙的前景对象掩模来提高训练效率和几何质量。我们还介绍了一种强大的正常估计技术，其消除了几何噪声的效果，同时保持了重要细节。最后，我们提取表面材料特性和环境照明，以球形谐波表示，具有处理瞬态元素的延伸部，例如，锋利的阴影。这些组件的结合导致高度模块化和有效的对象采集框架。广泛的评估和比较证明了我们在捕获高质量的几何形状和外观特性方面的方法，可用于渲染应用。