We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which represents a 3D voxel grid with per-voxel multi-channel features. Our central idea is to factorize the 4D scene tensor into multiple compact low-rank tensor components. We demonstrate that applying traditional CP decomposition -- that factorizes tensors into rank-one components with compact vectors -- in our framework leads to improvements over vanilla NeRF. To further boost performance, we introduce a novel vector-matrix (VM) decomposition that relaxes the low-rank constraints for two modes of a tensor and factorizes tensors into compact vector and matrix factors. Beyond superior rendering quality, our models with CP and VM decompositions lead to a significantly lower memory footprint in comparison to previous and concurrent works that directly optimize per-voxel features. Experimentally, we demonstrate that TensoRF with CP decomposition achieves fast reconstruction (<30 min) with better rendering quality and even a smaller model size (<4 MB) compared to NeRF. Moreover, TensoRF with VM decomposition further boosts rendering quality and outperforms previous state-of-the-art methods, while reducing the reconstruction time (<10 min) and retaining a compact model size (<75 MB).

Neural radiance field (NeRF) attracts attention as a promising approach to reconstructing the 3D scene. As NeRF emerges, subsequent studies have been conducted to model dynamic scenes, which include motions or topological changes. However, most of them use an additional deformation network, slowing down the training and rendering speed. Tensorial radiance field (TensoRF) recently shows its potential for fast, high-quality reconstruction of static scenes with compact model size. In this paper, we present D-TensoRF, a tensorial radiance field for dynamic scenes, enabling novel view synthesis at a specific time. We consider the radiance field of a dynamic scene as a 5D tensor. The 5D tensor represents a 4D grid in which each axis corresponds to X, Y, Z, and time and has 1D multi-channel features per element. Similar to TensoRF, we decompose the grid either into rank-one vector components (CP decomposition) or low-rank matrix components (newly proposed MM decomposition). We also use smoothing regularization to reflect the relationship between features at different times (temporal dependency). We conduct extensive evaluations to analyze our models. We show that D-TensoRF with CP decomposition and MM decomposition both have short training times and significantly low memory footprints with quantitatively and qualitatively competitive rendering results in comparison to the state-of-the-art methods in 3D dynamic scene modeling.

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.

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.

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。

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.

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.

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。

神经辐射场（NERF）是数据驱动3D重建中的流行方法。鉴于其简单性和高质量的渲染，正在开发许多NERF应用程序。但是，NERF的大量的速度很大。许多尝试如何加速NERF培训和推理，包括复杂的代码级优化和缓存，使用复杂的数据结构以及通过多任务和元学习的摊销。在这项工作中，我们通过NERF之前通过经典技术镜头重新审视NERF的基本构建块。我们提出了Voxel-Accelated Nerf（VaxnerF），与Visual Hull集成了Nerf，一种经典的3D重建技术，只需要每张图像的二进制前景背景像素标签。可视船体，可在大约10秒内优化，可以提供粗略的现场分离，以省略NERF中的大量网络评估。我们在流行的JAXNERF Codebase提供了一个干净的全力验光，基于JAX的实现，其仅包括大约30行的代码更改和模块化视觉船体子程序，并在高度表现的JAXNERF之上实现了大约2-8倍的速度学习基线具有零劣化呈现质量。具有足够的计算，这有效地将单位训练从小时到30分钟缩小到30分钟。我们希望VAXNERF - 一种仔细组合具有深入方法的经典技术（可谓更换它） - 可以赋予并加速新的NERF扩展和应用，以其简单，可移植性和可靠的性能收益。代码在https://github.com/naruya/vaxnerf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。

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.

Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers downstream tasks like spatial-relation-aware editing. In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. PVD consequently empowers downstream applications to optimally adapt the neural representations for the task at hand in a post hoc fashion. The conversions are fast, as distillation is progressively performed on different levels of volume representations, from shallower to deeper. We also employ special treatment of density to deal with its specific numerical instability problem. Empirical evidence is presented to validate our method on the NeRF-Synthetic, LLFF and TanksAndTemples datasets. For example, with PVD, an MLP-based NeRF model can be distilled from a hashtable-based Instant-NGP model at a 10X~20X faster speed than being trained the original NeRF from scratch, while achieving a superior level of synthesis quality. Code is available at https://github.com/megvii-research/AAAI2023-PVD.

本文提出了一种用等值的全向图像重建神经辐射场的方法。带有辐射场的隐式神经场景表示可以在有限的空间区域内连续重建场景的3D形状。但是，培训商用PC硬件的完全隐式表示需要大量时间和计算资源（15 $ \ sim $ 20小时每场景20小时）。因此，我们提出了一种显着加速此过程的方法（每个场景20 $ \ sim $ 40分钟）。我们采用特征体素，而不是使用辐射场重建的光线的完全隐式表示，而是在张量中包含密度和颜色特征的特征体素。考虑全向等值输入和相机布局，我们使用球形素化来表示表示而不是立方表示。我们的体素化方法可以平衡内部场景和外部场景的重建质量。此外，我们在颜色特征上采用了与轴对准的位置编码方法，以提高总图像质量。我们的方法可以在随机摄像头姿势上实现满足合成数据集的经验性能。此外，我们使用包含复杂几何形状并实现最先进性能的真实场景测试我们的方法。我们的代码和完整数据集将与纸质出版物同时发布。

我们提出了逐渐变化的辐射场（PDRF），这是一种从模糊图像中有效重建高质量辐射场的新方法。虽然当前的最先进的（SOTA）场景重建方法实现了光真实的渲染，因此清洁源视图会导致其性能在源视图受模糊影响的影响时会受到影响，这通常是野外图像的观察。以前的脱毛方法要么不考虑3D几何形状，要么是计算强度。为了解决这些问题，PDRF是Radiance Field建模中逐渐消除的方案，通过合并3D场景上下文来准确地模拟模糊。 PDRF进一步使用了有效的重要性采样方案，从而导致快速场景优化。具体而言，PDRF提出了一个粗射线渲染器，以快速估计体素密度和特征。然后，使用精细的体素渲染器来实现高质量的射线追踪。我们执行广泛的实验，并表明PDRF比以前的SOTA快15倍，同时在合成场景和真实场景上都取得更好的性能。

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。

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。

我们提出了一个小说嵌入字段\ emph {pref}作为促进神经信号建模和重建任务的紧凑表示。基于纯的多层感知器（MLP）神经技术偏向低频信号，并依赖于深层或傅立叶编码以避免丢失细节。取而代之的是，基于傅立叶嵌入空间的相拟合公式，PREF采用了紧凑且物理上解释的编码场。我们进行全面的实验，以证明PERF比最新的空间嵌入技术的优势。然后，我们使用近似的逆傅里叶变换方案以及新型的parseval正常器来开发高效的频率学习框架。广泛的实验表明，我们的高效和紧凑的基于频率的神经信号处理技术与2D图像完成，3D SDF表面回归和5D辐射场现场重建相同，甚至比最新的。

神经辐射场（NERFS）产生最先进的视图合成结果。然而，它们慢渲染，需要每像素数百个网络评估，以近似卷渲染积分。将nerfs烘烤到明确的数据结构中实现了有效的渲染，但导致内存占地面积的大幅增加，并且在许多情况下，质量降低。在本文中，我们提出了一种新的神经光场表示，相反，相反，紧凑，直接预测沿线的集成光线。我们的方法支持使用每个像素的单个网络评估，用于小基线光场数据集，也可以应用于每个像素的几个评估的较大基线。在我们的方法的核心，是一个光线空间嵌入网络，将4D射线空间歧管映射到中间可间可动子的潜在空间中。我们的方法在诸如斯坦福光场数据集等密集的前置数据集中实现了最先进的质量。此外，对于带有稀疏输入的面对面的场景，我们可以在质量方面实现对基于NERF的方法具有竞争力的结果，同时提供更好的速度/质量/内存权衡，网络评估较少。

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