神经辐射场(NERFS)表现出惊人的能力,可以从新颖的观点中综合3D场景的图像。但是,他们依赖于基于射线行进的专门体积渲染算法,这些算法与广泛部署的图形硬件的功能不匹配。本文介绍了基于纹理多边形的新的NERF表示形式,该表示可以有效地与标准渲染管道合成新型图像。 NERF表示为一组多边形,其纹理代表二进制不相处和特征向量。用Z-Buffer对多边形的传统渲染产生了每个像素的图像,该图像由在片段着色器中运行的小型,观点依赖的MLP来解释,以产生最终的像素颜色。这种方法使NERF可以使用传统的Polygon栅格化管道渲染,该管道提供了庞大的像素级并行性,从而在包括移动电话在内的各种计算平台上实现了交互式帧速率。
translated by 谷歌翻译
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.
translated by 谷歌翻译
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.
translated by 谷歌翻译
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。
translated by 谷歌翻译
我们提出了一种有效的方法,用于从多视图图像观察中联合优化拓扑,材料和照明。与最近的多视图重建方法不同,通常在神经网络中产生纠缠的3D表示,我们将三角形网格输出具有空间不同的材料和环境照明,这些方法可以在任何传统的图形引擎中未修改。我们利用近期工作在可差异化的渲染中,基于坐标的网络紧凑地代表体积纹理,以及可微分的游行四边形,以便直接在表面网上直接实现基于梯度的优化。最后,我们介绍了环境照明的分流和近似的可分辨率配方,以有效地回收全频照明。实验表明我们的提取模型用于高级场景编辑,材料分解和高质量的视图插值,全部以三角形的渲染器(光栅化器和路径示踪剂)的交互式速率运行。
translated by 谷歌翻译
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.
translated by 谷歌翻译
潜水员在NERF的关键思想和其变体 - 密度模型和体积渲染的关键思想中建立 - 学习可以从少量图像实际渲染的3D对象模型。与所有先前的NERF方法相比,潜水员使用确定性而不是体积渲染积分的随机估计。潜水员的表示是基于体素的功能领域。为了计算卷渲染积分,将光线分为间隔,每个体素;使用MLP的每个间隔的特征估计体渲染积分的组件,并且组件聚合。结果,潜水员可以呈现其他集成商错过的薄半透明结构。此外,潜水员的表示与其他这样的方法相比相对暴露的语义 - 在体素空间中的运动特征向量导致自然编辑。对当前最先进的方法的广泛定性和定量比较表明,潜水员产生(1)在最先进的质量或高于最先进的质量,(2)的情况下非常小而不会被烘烤,(3)在不被烘烤的情况下渲染非常快,并且(4)可以以自然方式编辑。
translated by 谷歌翻译
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...
translated by 谷歌翻译
Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intricate details and certain effects, such as subsurface scattering, elude representation using real-time BRDFs, making it impossible to fully capture the appearance of certain objects. Inspired by the recent progress of neural rendering, we propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct volumetric effects, such as translucent object parts, and preserve photorealistic object appearance. To support real-time rendering without compromising rendering quality, our model uses a grid of features and a small MLP decoder that is transpiled into efficient shader code with interactive framerates. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects. Thanks to the use of standard shader code rendering is portable across many existing hardware and software systems.
translated by 谷歌翻译
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。
translated by 谷歌翻译
NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images. However, NeRF requires querying a deep Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering times, even on modern GPUs. In this paper, we demonstrate that real-time rendering is possible by utilizing thousands of tiny MLPs instead of one single large MLP. In our setting, each individual MLP only needs to represent parts of the scene, thus smaller and faster-to-evaluate MLPs can be used. By combining this divide-and-conquer strategy with further optimizations, rendering is accelerated by three orders of magnitude compared to the original NeRF model without incurring high storage costs. Further, using teacher-student distillation for training, we show that this speed-up can be achieved without sacrificing visual quality.
translated by 谷歌翻译
我们介绍了一种超快速的收敛方法来重建从一组图像中捕获具有已知姿势的场景的图像的每场辐射场。该任务通常适用于新颖的视图综合,最近是由神经辐射领域(NERF)彻底改革为其最先进的质量和灵活性。然而,NERF及其变体需要漫长的训练时间来为单个场景的数小时到几天。相比之下,我们的方法实现了NERF相当的质量,并通过单个GPU在不到15分钟内从划痕中迅速收敛。我们采用由密度体素网格组成的表示,用于场景几何形状和具有浅网络的特征体素网格,用于复杂的视图依赖性外观。用明确和离散化卷表示的建模并不是新的,但我们提出了两种简单而非琐碎的技术,有助于快速收敛速度和高质量的输出。首先,我们介绍了体素密度的激活后插值,其能够以较低的网格分辨率产生尖锐的表面。其次,直接体素密度优化容易发生次优几何解决方案,因此我们通过施加多个前沿来强制优化过程。最后,对五个内向的基准评估表明,我们的方法匹配,如果没有超越Nerf的质量,但它只需15分钟即可从头开始训练新场景。
translated by 谷歌翻译
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
translated by 谷歌翻译
神经辐射场(NERF)在代表3D场景和合成新颖视图中示出了很大的潜力,但是在推理阶段的NERF的计算开销仍然很重。为了减轻负担,我们进入了NERF的粗细分,分层采样过程,并指出粗阶段可以被我们命名神经样本场的轻量级模块代替。所提出的示例场地图光线进入样本分布,可以将其转换为点坐标并进料到radiance字段以进行体积渲染。整体框架被命名为Neusample。我们在现实合成360 $ ^ {\ circ} $和真正的前瞻性,两个流行的3D场景集上进行实验,并表明Neusample在享受更快推理速度时比NERF实现更好的渲染质量。Neusample进一步压缩,以提出的样品场提取方法朝向质量和速度之间的更好的权衡。
translated by 谷歌翻译
对于场景重建和新型视图综合的数量表示形式的普及最近,人们的普及使重点放在以高视觉质量和实时为实时的体积内容动画上。尽管基于学习功能的隐性变形方法可以产生令人印象深刻的结果,但它们是艺术家和内容创建者的“黑匣子”,但它们需要大量的培训数据才能有意义地概括,并且在培训数据之外不会产生现实的外推。在这项工作中,我们通过引入实时的音量变形方法来解决这些问题,该方法是实时的,易于使用现成的软件编辑,并且可以令人信服地推断出来。为了证明我们方法的多功能性,我们将其应用于两种情况:基于物理的对象变形和触发性,其中使用Blendshapes控制着头像。我们还进行了彻底的实验,表明我们的方法与两种体积方法相比,结合了基于网格变形的隐式变形和方法。
translated by 谷歌翻译
重建反向渲染技术的最新趋势使用神经网络将3D表示作为神经领域。基于NERF的技术将多层感知器(MLP)拟合到一组训练图像,以估算一个辐射场字段,然后可以通过卷渲染算法从任何虚拟摄像机呈现。这些表示形式的主要缺点是缺乏定义明确的表面和非交互式渲染时间,因为必须查询宽大和深的MLP,每个框架必须查询数百万次。这些限制最近被单一克服了,但是设法同时完成了这一限制,从而打开了新的用例。我们提出了Kiloneus,这是一种新的神经对象表示,可以在交互式框架速率下的路径跟踪场景中渲染。 Kiloneus可以在共享场景中对神经和经典原语之间的逼真的光相互作用进行模拟,并且它可以实时执行,并有足够的空间进行未来的优化和扩展。
translated by 谷歌翻译
Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow, limiting the application scenarios of utilizing NeRF on resource-constrained hardware, such as mobile devices. Many works have been conducted to reduce the latency of running NeRF models. However, most of them still require high-end GPU for acceleration or extra storage memory, which is all unavailable on mobile devices. Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. Nevertheless, to reach a similar rendering quality as NeRF, the network in NeLF is designed with intensive computation, which is not mobile-friendly. In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering. We follow the setting of NeLF to train our network. Unlike existing works, we introduce a novel network architecture that runs efficiently on mobile devices with low latency and small size, i.e., saving $15\times \sim 24\times$ storage compared with MobileNeRF. Our model achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world scenes on mobile devices, e.g., $18.04$ms (iPhone 13) for rendering one $1008\times756$ image of real 3D scenes. Additionally, we achieve similar image quality as NeRF and better quality than MobileNeRF (PSNR $26.15$ vs. $25.91$ on the real-world forward-facing dataset).
translated by 谷歌翻译
Google Research Basecolor Metallic Roughness Normal Multi-View Images NeRD Volume Decomposed BRDF Relighting & View synthesis Textured MeshFigure 1: Neural Reflectance Decomposition for Relighting. We encode multiple views of an object under varying or fixed illumination into the NeRD volume.We decompose each given image into geometry, spatially-varying BRDF parameters and a rough approximation of the incident illumination in a globally consistent manner. We then extract a relightable textured mesh that can be re-rendered under novel illumination conditions in real-time.
translated by 谷歌翻译
Recent advances in Neural Radiance Fields (NeRFs) treat the problem of novel view synthesis as Sparse Radiance Field (SRF) optimization using sparse voxels for efficient and fast rendering (plenoxels,InstantNGP). In order to leverage machine learning and adoption of SRFs as a 3D representation, we present SPARF, a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of $\sim$ 17 million images rendered from nearly 40,000 shapes at high resolution (400 X 400 pixels). The dataset is orders of magnitude larger than existing synthetic datasets for novel view synthesis and includes more than one million 3D-optimized radiance fields with multiple voxel resolutions. Furthermore, we propose a novel pipeline (SuRFNet) that learns to generate sparse voxel radiance fields from only few views. This is done by using the densely collected SPARF dataset and 3D sparse convolutions. SuRFNet employs partial SRFs from few/one images and a specialized SRF loss to learn to generate high-quality sparse voxel radiance fields that can be rendered from novel views. Our approach achieves state-of-the-art results in the task of unconstrained novel view synthesis based on few views on ShapeNet as compared to recent baselines. The SPARF dataset will be made public with the code and models on the project website https://abdullahamdi.com/sparf/ .
translated by 谷歌翻译
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.
translated by 谷歌翻译