基于3D点云表示的视图合成方法已证明有效性。但是，现有的方法通常仅从单个源视图中综合新视图，并且概括它们以处理多个源视图以追求更高的重建质量是不平凡的。在本文中，我们提出了一种新的基于深度学习的视图综合范式，该范式从不同的源视图中学习了统一的3D点云。具体而言，我们首先通过根据其深度图将源视图投影到3D空间来构建子点云。然后，我们通过在子点云联合定义的本地社区中自适应地融合点来学习统一的3D点云。此外，我们还提出了一个3D几何引导的图像恢复模块，以填充孔并恢复渲染的新型视图的高频细节。三个基准数据集的实验结果表明，我们的方法在数量和视觉上都在很大程度上优于最先进的综合方法。

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。

This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resolution cameras. The performance of existing methods is still limited, as they produce either blurry results on plain textured areas or distortions around depth discontinuous boundaries. To tackle this challenge, we propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input from two complementary and parallel perspectives. Specifically, one module regresses a spatially consistent intermediate estimation by learning a deep multidimensional and cross-domain feature representation, while the other module warps another intermediate estimation, which maintains the high-frequency textures, by propagating the information of the high-resolution view. We finally leverage the advantages of the two intermediate estimations adaptively via the learned attention maps, leading to the final high-resolution LF image with satisfactory results on both plain textured areas and depth discontinuous boundaries. Besides, to promote the effectiveness of our method trained with simulated hybrid data on real hybrid data captured by a hybrid LF imaging system, we carefully design the network architecture and the training strategy. Extensive experiments on both real and simulated hybrid data demonstrate the significant superiority of our approach over state-of-the-art ones. To the best of our knowledge, this is the first end-to-end deep learning method for LF reconstruction from a real hybrid input. We believe our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission.

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.

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。

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。

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.

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.

在不同观点之间找到准确的对应关系是无监督的多视图立体声（MVS）的跟腱。现有方法是基于以下假设：相应的像素具有相似的光度特征。但是，在实际场景中，多视图图像观察到非斜面的表面和经验遮挡。在这项工作中，我们提出了一种新颖的方法，即神经渲染（RC-MVSNET），以解决观点之间对应关系的歧义问题。具体而言，我们施加了一个深度渲染一致性损失，以限制靠近对象表面的几何特征以减轻遮挡。同时，我们引入了参考视图综合损失，以产生一致的监督，即使是针对非兰伯特表面。关于DTU和TANKS \＆Temples基准测试的广泛实验表明，我们的RC-MVSNET方法在无监督的MVS框架上实现了最先进的性能，并对许多有监督的方法进行了竞争性能。该代码在https://github.com/上发布。 BOESE0601/RC-MVSNET

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.

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。

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。

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。

We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the 3D cost volume upon the reference camera frustum via the differentiable homography warping. Next, we apply 3D convolutions to regularize and regress the initial depth map, which is then refined with the reference image to generate the final output. Our framework flexibly adapts arbitrary N-view inputs using a variance-based cost metric that maps multiple features into one cost feature. The proposed MVSNet is demonstrated on the large-scale indoor DTU dataset. With simple post-processing, our method not only significantly outperforms previous state-of-the-arts, but also is several times faster in runtime. We also evaluate MVSNet on the complex outdoor Tanks and Temples dataset, where our method ranks first before April 18, 2018 without any fine-tuning, showing the strong generalization ability of MVSNet.

近年来，与传统方法相比，受监督或无监督的基于学习的MVS方法的性能出色。但是，这些方法仅使用成本量正规化计算的概率量来预测参考深度，这种方式无法从概率量中挖掘出足够的信息。此外，无监督的方法通常尝试使用两步或其他输入进行训练，从而使过程更加复杂。在本文中，我们提出了DS-MVSNET，这是一种具有源深度合成的端到端无监督的MVS结构。为了挖掘概率量的信息，我们通过将概率量和深度假设推向源视图来创造性地综合源深度。同时，我们提出了自适应高斯采样和改进的自适应垃圾箱采样方法，以改善深度假设精度。另一方面，我们利用源深度渲染参考图像，并提出深度一致性损失和深度平滑度损失。这些可以根据不同视图的光度和几何一致性提供其他指导，而无需其他输入。最后，我们在DTU数据集和储罐数据集上进行了一系列实验，这些实验证明了与最先进的方法相比，DS-MVSNET的效率和鲁棒性。

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.

Point of View & TimeFigure 1: We propose D-NeRF, a method for synthesizing novel views, at an arbitrary point in time, of dynamic scenes with complex non-rigid geometries. We optimize an underlying deformable volumetric function from a sparse set of input monocular views without the need of ground-truth geometry nor multi-view images. The figure shows two scenes under variable points of view and time instances synthesised by the proposed model.

最近的神经人类表示可以产生高质量的多视图渲染，但需要使用密集的多视图输入和昂贵的培训。因此，它们在很大程度上仅限于静态模型，因为每个帧都是不可行的。我们展示了人类学 - 一种普遍的神经表示 - 用于高保真自由观察动态人类的合成。类似于IBRNET如何通过避免每场景训练来帮助NERF，Humannerf跨多视图输入采用聚合像素对准特征，以及用于解决动态运动的姿势嵌入的非刚性变形场。原始人物员已经可以在稀疏视频输入的稀疏视频输入上产生合理的渲染。为了进一步提高渲染质量，我们使用外观混合模块增强了我们的解决方案，用于组合神经体积渲染和神经纹理混合的益处。各种多视图动态人类数据集的广泛实验证明了我们在挑战运动中合成照片 - 现实自由观点的方法和非常稀疏的相机视图输入中的普遍性和有效性。

我们提出了一个基于变压器的NERF（Transnerf），以学习在新视图合成任务的观察视图图像上进行的通用神经辐射场。相比之下，现有的基于MLP的NERF无法直接接收具有任意号码的观察视图，并且需要基于辅助池的操作来融合源视图信息，从而导致源视图与目标渲染视图之间缺少复杂的关系。此外，当前方法分别处理每个3D点，忽略辐射场场景表示的局部一致性。这些局限性可能会在挑战现实世界应用中降低其性能，在这些应用程序中可能存在巨大的差异和新颖的渲染视图之间的巨大差异。为了应对这些挑战，我们的Transnerf利用注意机制自然地将任意数量的源视图的深层关联解码为基于坐标的场景表示。在统一变压器网络中，在射线铸造空间和周围视图空间中考虑了形状和外观的局部一致性。实验表明，与基于图像的最先进的基于图像的神经渲染方法相比，我们在各种场景上接受过培训的Transnf可以在场景 - 敏捷和每个场景的燃烧场景中获得更好的性能。源视图与渲染视图之间的差距很大。

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.