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.

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/。

我们提出了一种方法，可以在神经SDF渲染器中相对于几何场景参数自动计算正确的梯度。最近基于物理的可区分渲染技术用于网格采样来处理不连续性，尤其是在对象轮廓上，但是SDF没有简单的参数形式，可用于采样。取而代之的是，我们的方法建立在区域采样技术的基础上，并为SDFS开发了连续的翘曲功能，以解决这些不连续性。我们的方法利用了在SDF中编码的表面的距离，并在球形示踪剂点上使用正交来计算此翘曲功能。我们进一步表明，这可以通过对要点进行次采样来使神经SDF的方法进行。我们可区分的渲染器可用于优化从多视图图像中的神经形状，并对最近基于SDF的反向渲染方法产生可比较的3D重建，而无需2D分割掩码来指导几何形状优化，而无需对几何形状进行体积近似。

我们提出了一种从单个图像中编辑复杂室内照明的方法，其深度和光源分割掩码。这是一个极具挑战性的问题，需要对复杂的光传输进行建模，并仅通过对场景的部分LDR观察，将HDR照明从材料和几何形状中解散。我们使用两个新颖的组件解决了这个问题：1）一种整体场景重建方法，该方法估计场景反射率和参数3D照明，以及2）一个神经渲染框架，从我们的预测中重新呈现场景。我们使用基于物理的室内光表示，可以进行直观的编辑，并推断可见和看不见的光源。我们的神经渲染框架结合了基于物理的直接照明和阴影渲染，深层网络近似于全球照明。它可以捕获具有挑战性的照明效果，例如柔软的阴影，定向照明，镜面材料和反射。以前的单个图像逆渲染方法通常纠缠场景照明和几何形状，仅支持对象插入等应用程序。取而代之的是，通过将参数3D照明估计与神经场景渲染相结合，我们演示了从单个图像中实现完整场景重新确定（包括光源插入，删除和替换）的第一种自动方法。所有源代码和数据将公开发布。

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.

The rapid growth of machine translation (MT) systems has necessitated comprehensive studies to meta-evaluate evaluation metrics being used, which enables a better selection of metrics that best reflect MT quality. Unfortunately, most of the research focuses on high-resource languages, mainly English, the observations for which may not always apply to other languages. Indian languages, having over a billion speakers, are linguistically different from English, and to date, there has not been a systematic study of evaluating MT systems from English into Indian languages. In this paper, we fill this gap by creating an MQM dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems, and use it to establish correlations between annotator scores and scores obtained using existing automatic metrics. Our results show that pre-trained metrics, such as COMET, have the highest correlations with annotator scores. Additionally, we find that the metrics do not adequately capture fluency-based errors in Indian languages, and there is a need to develop metrics focused on Indian languages. We hope that our dataset and analysis will help promote further research in this area.

Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many applications such as climate change adaptation planning and insurance underwriting. This research assesses the predictive capability of regressors constructed on the National Flood Insurance Program (NFIP) dataset using neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Process). The assessment highlights the most informative predictors for regression. The distribution for claims amount inference is modeled with a Burr distribution permitting the introduction of a bias correction scheme and increasing the regressor's predictive capability. Aiming to study the interaction with physical variables, we incorporate Daymet rainfall estimation to NFIP as an additional predictor. A study on the coastal counties in the eight US South-West states resulted in an $R^2=0.807$. Further analysis of 11 counties with a significant number of claims in the NFIP dataset reveals that Extreme Gradient Boosting provides the best results, that bias correction significantly improves the similarity with the reference distribution, and that the rainfall predictor strengthens the regressor performance.

Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: a) it allows for online real-time monitoring of a dynamical system; b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; c) it is computationally fast and less intensive than the popular Extended Dynamic Mode Decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowd workers are able to (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground the outcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of 8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue (Participant Outcome Questions), enables the exploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant's influence over the event culmination.

Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands detail that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution ($0.25^{\circ} \times 0.25^{\circ}$) climate model outputs into high-resolution ($0.01^{\circ} \times 0.01^{\circ}$) rainfall fields while efficaciously quantifying uncertainty. Results indicate that the downscaled rainfall fields closely match observed spatial fields and their risk distributions.