神经辐射字段（NERF）将场景编码为神经表示，使得能够实现新颖视图的照片逼真。然而，RGB图像的成功重建需要在静态条件下拍摄的大量输入视图 - 通常可以为房间尺寸场景的几百个图像。我们的方法旨在将整个房间的小说视图从数量级的图像中合成。为此，我们利用密集的深度前导者来限制NERF优化。首先，我们利用从用于估计相机姿势的运动（SFM）预处理步骤的结构自由提供的稀疏深度数据。其次，我们使用深度完成将这些稀疏点转换为密集的深度图和不确定性估计，用于指导NERF优化。我们的方法使数据有效的新颖观看综合在挑战室内场景中，使用少量为整个场景的18张图像。

神经辐射场（NERF）具有密集捕获的输入图像实现光真实的视图合成。然而，鉴于稀疏的视图，NERF的几何形状极为严重，从而导致新观点合成质量的显着降解。受到自我监督的深度估计方法的启发，我们提出了structnerf，这是针对稀疏输入的室内场景的新型视图合成的解决方案。 structnerf利用自然嵌入多视图输入中的结构提示来处理NERF中无约束的几何问题。具体而言，它分别解决了纹理和非纹理区域：提出了基于贴片的多视图一致的光度损失来限制纹理区域的几何形状；对于非纹理的，我们明确地将它们限制为3D一致的平面。通过密集的自我监督深度约束，我们的方法可以改善NERF的几何形状和视图综合性能，而无需对外部数据进行任何其他培训。在几个现实世界数据集上进行的广泛实验表明，构造者超过了针对室内场景的最新方法，这些方法具有稀疏输入的定量和定性。

神经场景表示，例如神经辐射场（NERF），基于训练多层感知器（MLP），使用一组具有已知姿势的彩色图像。现在，越来越多的设备产生RGB-D（颜色 +深度）信息，这对于各种任务非常重要。因此，本文的目的是通过将深度信息与颜色图像结合在一起，研究这些有希望的隐式表示可以进行哪些改进。特别是，最近建议的MIP-NERF方法使用圆锥形的圆丝而不是射线进行音量渲染，它使人们可以考虑具有距离距离摄像头中心距离的像素的不同区域。所提出的方法还模拟了深度不确定性。这允许解决基于NERF的方法的主要局限性，包括提高几何形状的准确性，减少伪像，更快的训练时间和缩短预测时间。实验是在众所周知的基准场景上进行的，并且比较在场景几何形状和光度重建中的准确性提高，同时将训练时间减少了3-5次。

获取房间规模场景的高质量3D重建对于即将到来的AR或VR应用是至关重要的。这些范围从混合现实应用程序进行电话会议，虚拟测量，虚拟房间刨，到机器人应用。虽然使用神经辐射场（NERF）的基于卷的视图合成方法显示有希望再现对象或场景的外观，但它们不会重建实际表面。基于密度的表面的体积表示在使用行进立方体提取表面时导致伪影，因为在优化期间，密度沿着射线累积，并且不在单个样本点处于隔离点。我们建议使用隐式函数（截短的签名距离函数）来代表表面来代表表面。我们展示了如何在NERF框架中纳入此表示，并将其扩展为使用来自商品RGB-D传感器的深度测量，例如Kinect。此外，我们提出了一种姿势和相机细化技术，可提高整体重建质量。相反，与集成NERF的深度前瞻性的并发工作，其专注于新型视图合成，我们的方法能够重建高质量的韵律3D重建。

由于其简单性和最先进的性能，神经辐射场（NERF）被出现为新型视图综合任务的强大表示。虽然NERF可以在许多输入视图可用时产生看不见的观点的光静观渲染，但是当该数量减少时，其性能显着下降。我们观察到，稀疏输入方案中的大多数伪像是由估计场景几何中的错误引起的，并且在训练开始时通过不同的行为引起。我们通过规范从未观察的视点呈现的修补程序的几何和外观来解决这一点，并在训练期间退火光线采样空间。我们还使用规范化的流模型来规范未观察的视点的颜色。我们的车型不仅优于优化单个场景的其他方法，而是在许多情况下，还有条件模型，这些模型在大型多视图数据集上广泛预先培训。

在许多计算机视觉和图形应用程序中，从2D图像重建3D室内场景是一项重要任务。这项任务中的一个主要挑战是，典型的室内场景中的无纹理区域使现有方法难以产生令人满意的重建结果。我们提出了一种名为Neuris的新方法，以高质量地重建室内场景。 Neuris的关键思想是将估计的室内场景正常整合为神经渲染框架中的先验，以重建大型无纹理形状，并且重要的是，以适应性的方式进行此操作，以便重建不规则的形状，并具有很好的细节。 。具体而言，我们通过检查优化过程中重建的多视图一致性来评估正常先验的忠诚。只有被接受为忠实的正常先验才能用于3D重建，通常发生在平滑形状的区域中，可能具有弱质地。但是，对于那些具有小物体或薄结构的区域，普通先验通常不可靠，我们只能依靠输入图像的视觉特征，因为此类区域通常包含相对较丰富的视觉特征（例如，阴影变化和边界轮廓）。广泛的实验表明，在重建质量方面，Neuris明显优于最先进的方法。

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.

Training a Neural Radiance Field (NeRF) without pre-computed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy.

这项工作的目标是通过扫描平台捕获的数据进行3D重建和新颖的观看综合，该平台在城市室外环境中常设世界映射（例如，街景）。给定一系列由摄像机和扫描仪通过室外场景的摄像机和扫描仪进行的序列，我们产生可以从中提取3D表面的模型，并且可以合成新颖的RGB图像。我们的方法扩展了神经辐射字段，已经证明了用于在受控设置中的小型场景中的逼真新颖的图像，用于利用异步捕获的LIDAR数据，用于寻址捕获图像之间的曝光变化，以及利用预测的图像分段来监督密度。在光线指向天空。这三个扩展中的每一个都在街道视图数据上的实验中提供了显着的性能改进。我们的系统产生最先进的3D表面重建，并与传统方法（例如〜Colmap）和最近的神经表示（例如〜MIP-NERF）相比，合成更高质量的新颖视图。

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.

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。

We present Depth-aware Image-based NEural Radiance fields (DINER). Given a sparse set of RGB input views, we predict depth and feature maps to guide the reconstruction of a volumetric scene representation that allows us to render 3D objects under novel views. Specifically, we propose novel techniques to incorporate depth information into feature fusion and efficient scene sampling. In comparison to the previous state of the art, DINER achieves higher synthesis quality and can process input views with greater disparity. This allows us to capture scenes more completely without changing capturing hardware requirements and ultimately enables larger viewpoint changes during novel view synthesis. We evaluate our method by synthesizing novel views, both for human heads and for general objects, and observe significantly improved qualitative results and increased perceptual metrics compared to the previous state of the art. The code will be made publicly available for research purposes.

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.

我们提出了一种基于神经辐射场（NERF）的单个$ 360^\ PANORAMA图像合成新视图的方法。在类似环境中的先前研究依赖于多层感知的邻居插值能力来完成由遮挡引起的丢失区域，这导致其预测中的伪像。我们提出了360Fusionnerf，这是一个半监督的学习框架，我们介绍几何监督和语义一致性，以指导渐进式培训过程。首先，将输入图像重新投影至$ 360^\ Circ $图像，并在其他相机位置提取辅助深度图。除NERF颜色指导外，深度监督还改善了合成视图的几何形状。此外，我们引入了语义一致性损失，鼓励新观点的现实渲染。我们使用预先训练的视觉编码器（例如剪辑）提取这些语义功能，这是一个视觉变压器，经过数以千计的不同2D照片，并通过自然语言监督从网络中挖掘出来。实验表明，我们提出的方法可以在保留场景的特征的同时产生未观察到的区域的合理完成。 360fusionnerf在各种场景中接受培训时，转移到合成结构3D数据集（PSNR〜5％，SSIM〜3％lpips〜13％）时，始终达到最先进的性能，SSIM〜3％LPIPS〜9％）和replica360数据集（PSNR〜8％，SSIM〜2％LPIPS〜18％）。

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multilayer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.

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。

神经辐射场（NERF）的最新进展实现了最新的新型视图合成，并促进了场景特性的密集估计。但是，在非常稀疏的视图下捕获的大型无界场景通常会失败，而场景内容集中在远离相机的情况下，这是典型的现场机器人应用程序。特别是，NERF风格的算法的性能很差：（1）当视图不足而呈姿势多样性的情况不足时，（2）当场景包含饱和度和阴影时，以及（3）当对具有精细结构的大型无界场景进行精心采样时，计算中就会大量强度。本文提出了克隆器，它通过允许从稀疏输入传感器视图中观察到的大型户外驾驶场景来对NERF进行显着改善。这是通过将NERF框架内的占用和颜色学习分离成分别使用LIDAR和相机数据训练的单独的多层感知器（MLP）来实现的。此外，本文提出了一种新的方法，可以在NERF模型旁边构建可区分的3D占用网格图（OGM），并利用此占用网格来改进沿射线的点采样，以在度量空间中进行体积渲染。通过在Kitti数据集的场景上进行的广泛定量和定性实验，本文表明，在新的视图合成和密集的深度预测任务上对稀疏输入数据培训时，所提出的方法在新型视图合成和密集的深度预测任务上都优于最先进的NERF模型。

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

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.

With the success of neural volume rendering in novel view synthesis, neural implicit reconstruction with volume rendering has become popular. However, most methods optimize per-scene functions and are unable to generalize to novel scenes. We introduce VolRecon, a generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct with fine details and little noise, we combine projection features, aggregated from multi-view features with a view transformer, and volume features interpolated from a coarse global feature volume. A ray transformer computes SRDF values of all the samples along a ray to estimate the surface location, which are used for volume rendering of color and depth. Extensive experiments on DTU and ETH3D demonstrate the effectiveness and generalization ability of our method. On DTU, our method outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable quality as MVSNet in full view reconstruction. Besides, our method shows good generalization ability on the large-scale ETH3D benchmark. Project page: https://fangjinhuawang.github.io/VolRecon.