Stylized view generation of scenes captured casually using a camera has received much attention recently. The geometry and appearance of the scene are typically captured as neural point sets or neural radiance fields in the previous work. An image stylization method is used to stylize the captured appearance by training its network jointly or iteratively with the structure capture network. The state-of-the-art SNeRF method trains the NeRF and stylization network in an alternating manner. These methods have high training time and require joint optimization. In this work, we present StyleTRF, a compact, quick-to-optimize strategy for stylized view generation using TensoRF. The appearance part is fine-tuned using sparse stylized priors of a few views rendered using the TensoRF representation for a few iterations. Our method thus effectively decouples style-adaption from view capture and is much faster than the previous methods. We show state-of-the-art results on several scenes used for this purpose.

3D场景感性风格化旨在根据给定的样式图像从任意新颖的视图中生成光真逼真的图像，同时在从不同观点呈现时确保一致性。一些带有神经辐射场的现有风格化方法可以通过将样式图像的特征与多视图图像结合到训练3D场景来有效地预测风格化的场景。但是，这些方法生成了包含令人反感的伪影的新型视图图像。此外，他们无法为3D场景实现普遍的影迷风格化。因此，样式图像必须根据神经辐射场重新训练3D场景表示网络。我们提出了一个新颖的3D场景，逼真的风格转移框架来解决这些问题。它可以通过2D样式图像实现感性3D场景样式转移。我们首先预先训练了2D逼真的样式传输网络，该网络可以符合任何给定内容图像和样式图像之间的影片风格转移。然后，我们使用体素特征来优化3D场景并获得场景的几何表示。最后，我们共同优化了一个超级网络，以实现场景的逼真风格传输的任意样式图像。在转移阶段，我们使用预先训练的2D影视网络来限制3D场景中不同视图和不同样式图像的感性风格。实验结果表明，我们的方法不仅实现了任意样式图像的3D影像风格转移，而且还优于视觉质量和一致性方面的现有方法。项目页面：https：//semchan.github.io/upst_nerf。

本文提出了一种程式化的新型视图合成方法。将最新的风格化方法应用于新型视图框架上，通常由于缺乏跨视图一致性而引起抖动的伪像。因此，本文研究了3D场景样式，该风格为一致的新型视图综合提供了强烈的诱导偏置。具体而言，我们采用新兴的神经光辉领域（NERF）作为我们选择的3D场景表示，因为它们有能力为各种场景提供高质量的新颖观点。但是，由于从NERF呈现新颖的视图需要大量样品，因此训练风格化的NERF需要大量的GPU内存，这超出了现成的GPU容量。我们引入了一种新的培训方法，通过交替进行NERF和样式优化步骤来解决此问题。这样的方法使我们能够充分利用自己的硬件记忆能力以更高的分辨率生成图像，又采用更具表现力的图像样式传输方法。我们的实验表明，我们的方法生成了针对各种内容的风格化的NERF，包括室内，室外和动态场景，并综合具有跨视图一致性的高质量小说视图。

我们提出了一种将任意样式图像的艺术特征转移到3D场景的方法。在点云或网格上执行3D风格的先前方法对复杂的现实世界场景的几何重建错误敏感。取而代之的是，我们建议对更健壮的辐射场字段表示。我们发现，常用的基于克矩阵的损失倾向于在没有忠实笔触的情况下产生模糊的结果，并引入了最近的基于邻居的损失，该损失非常有效地捕获样式的细节，同时保持多视图一致性。我们还提出了一种新颖的递延后传播方法，以使用在全分辨率渲染图像上定义的样式损失来优化记忆密集型辐射场。我们广泛的评估表明，我们的方法通过产生与样式图像更相似的艺术外观来优于基线。请检查我们的项目页面以获取视频结果和开源实现：https：//www.cs.cornell.edu/projects/arf/。

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.

通过隐式表示表示视觉信号（例如，基于坐标的深网）在许多视觉任务中都占了上风。这项工作探讨了一个新的有趣的方向：使用可以适用于各种2D和3D场景的广义方法训练风格化的隐式表示。我们对各种隐式函数进行了试点研究，包括基于2D坐标的表示，神经辐射场和签名距离函数。我们的解决方案是一个统一的隐式神经风化框架，称为INS。与Vanilla隐式表示相反，INS将普通隐式函数分解为样式隐式模块和内容隐式模块，以便从样式图像和输入场景中分别编码表示表示。然后，应用合并模块来汇总这些信息并合成样式化的输出。为了使3D场景中的几何形状进行正规化，我们提出了一种新颖的自我鉴定几何形状一致性损失，该损失保留了风格化场景的几何忠诚度。全面的实验是在多个任务设置上进行的，包括对复杂场景的新型综合，隐式表面的风格化以及使用MLP拟合图像。我们进一步证明，学到的表示不仅是连续的，而且在风格上都是连续的，从而导致不同样式之间毫不费力地插值，并以新的混合样式生成图像。请参阅我们的项目页面上的视频以获取更多查看综合结果：https：//zhiwenfan.github.io/ins。

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。

Figure 1: Our method can synthesize novel views in both space and time from a single monocular video of a dynamic scene. Here we show video results with various configurations of fixing and interpolating view and time (left), as well as a visualization of the recovered scene geometry (right). Please view with Adobe Acrobat or KDE Okular to see animations.

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...

As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially on simulating a text-guided style with both the appearance and the geometry altered simultaneously. In this paper, we present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt. Unlike previous approaches that either lack sufficient geometry deformations and texture details or require meshes to guide the stylization, our method can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance. This is achieved by introducing a novel global-local contrastive learning strategy, combined with the directional constraint to simultaneously control both the trajectory and the strength of the target style. Moreover, we adopt a weight regularization method to effectively suppress cloudy artifacts and geometry noises which arise easily when the density field is transformed during geometry stylization. Through extensive experiments on various styles, we demonstrate that our method is effective and robust regarding both single-view stylization quality and cross-view consistency. The code and more results can be found in our project page: https://cassiepython.github.io/nerfart/.

我们提出了高动态范围辐射（HDR）字段，HDR-PLENOXELS，它学习了3D HDR辐射场的肺化功能，几何信息和2D低动态范围（LDR）图像中固有的不同摄像机设置。我们基于体素的卷渲染管道可重建HDR辐射字段，仅以端到端的方式从不同的相机设置中拍摄的多视图LDR图像，并且具有快速的收敛速度。为了在现实世界中处理各种摄像机，我们引入了一个音调映射模块，该模块模拟了数字相机内成像管道（ISP）（ISP）和DISTANGLES辐射测定设置。我们的音调映射模块可以通过控制每个新型视图的辐射设置来渲染。最后，我们构建一个具有不同摄像机条件的多视图数据集，适合我们的问题设置。我们的实验表明，HDR-Plenoxels可以从具有各种相机的LDR图像中表达细节和高质量的HDR新型视图。

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。

Radiance Fields (RF) are popular to represent casually-captured scenes for new view generation and have been used for applications beyond it. Understanding and manipulating scenes represented as RFs have to naturally follow to facilitate mixed reality on personal spaces. Semantic segmentation of objects in the 3D scene is an important step for that. Prior segmentation efforts using feature distillation show promise but don't scale to complex objects with diverse appearance. We present a framework to interactively segment objects with fine structure. Nearest neighbor feature matching identifies high-confidence regions of the objects using distilled features. Bilateral filtering in a joint spatio-semantic space grows the region to recover accurate segmentation. We show state-of-the-art results of segmenting objects from RFs and compositing them to another scene, changing appearance, etc., moving closer to rich scene manipulation and understanding. Project Page: https://rahul-goel.github.io/isrf/

我们提出了Panohdr-nerf，这是一种新颖的管道，可随意捕获大型室内场景的合理的全HDR辐射场，而无需精心设计或复杂的捕获协议。首先，用户通过在场景中自由挥舞现成的摄像头来捕获场景的低动态范围（LDR）全向视频。然后，LDR2HDR网络将捕获的LDR帧提升到HDR，随后用于训练定制的NERF ++模型。由此产生的Panohdr-NERF管道可以从场景的任何位置估算完整的HDR全景。通过在一个新的测试数据集上进行各种真实场景的实验，并在训练过程中未见的位置捕获了地面真相HDR辐射，我们表明PanoHDR-NERF可以预测任何场景点的合理辐射。我们还表明，PanoHDR-NERF产生的HDR图像可以合成正确的照明效果，从而可以使用正确点亮的合成对象来增强室内场景。

我们引入了一个可扩展的框架，用于从RGB-D图像中具有很大不完整的场景覆盖率的新型视图合成。尽管生成的神经方法在2D图像上表现出了惊人的结果，但它们尚未达到相似的影像学结果，并结合了场景完成，在这种情况下，空间3D场景的理解是必不可少的。为此，我们提出了一条在基于网格的神经场景表示上执行的生成管道，通过以2.5D-3D-2.5D方式进行场景的分布来完成未观察到的场景部分。我们在3D空间中处理编码的图像特征，并具有几何完整网络和随后的纹理镶嵌网络，以推断缺失区域。最终可以通过与一致性的可区分渲染获得感性图像序列。全面的实验表明，我们方法的图形输出优于最新技术，尤其是在未观察到的场景部分中。

Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the appearance of such representations can be efficiently edited while maintaining photorealism. In this work, we present PaletteNeRF, a novel method for photorealistic appearance editing of neural radiance fields (NeRF) based on 3D color decomposition. Our method decomposes the appearance of each 3D point into a linear combination of palette-based bases (i.e., 3D segmentations defined by a group of NeRF-type functions) that are shared across the scene. While our palette-based bases are view-independent, we also predict a view-dependent function to capture the color residual (e.g., specular shading). During training, we jointly optimize the basis functions and the color palettes, and we also introduce novel regularizers to encourage the spatial coherence of the decomposition. Our method allows users to efficiently edit the appearance of the 3D scene by modifying the color palettes. We also extend our framework with compressed semantic features for semantic-aware appearance editing. We demonstrate that our technique is superior to baseline methods both quantitatively and qualitatively for appearance editing of complex real-world scenes.

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。

We address the problem of synthesizing novel views from a monocular video depicting a complex dynamic scene. State-of-the-art methods based on temporally varying Neural Radiance Fields (aka dynamic NeRFs) have shown impressive results on this task. However, for long videos with complex object motions and uncontrolled camera trajectories, these methods can produce blurry or inaccurate renderings, hampering their use in real-world applications. Instead of encoding the entire dynamic scene within the weights of an MLP, we present a new approach that addresses these limitations by adopting a volumetric image-based rendering framework that synthesizes new viewpoints by aggregating features from nearby views in a scene-motion-aware manner. Our system retains the advantages of prior methods in its ability to model complex scenes and view-dependent effects, but also enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories. We demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets, and also apply our approach to in-the-wild videos with challenging camera and object motion, where prior methods fail to produce high-quality renderings. Our project webpage is at dynibar.github.io.

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

b) MVS-NeRF no fine-tuning c) MVS-NeRF 6 min fine-tuning d) NeRF 5.1h optimization a) Source views SSIM:0.766 SSIM: 0.923 SSIM:0.924 * Equal contribution Research done when Anpei Chen was in a remote internship with UCSD.generalizable radiance field reconstruction. Moreover, if dense images are captured, our estimated radiance field representation can be easily fine-tuned; this leads to fast per-scene reconstruction with higher rendering quality and substantially less optimization time than NeRF.