In this paper, we present GaNI, a Global and Near-field Illumination-aware
neural inverse rendering technique that can reconstruct geometry, albedo, and
roughness parameters from images of a scene captured with co-located light and
camera. Existing inverse rendering techniques with co-located light-camera
focus on single objects only, without modeling global illumination and
near-field lighting more prominent in scenes with multiple objects. We
introduce a system that solves this problem in two stages; we first reconstruct
the geometry powered by neural volumetric rendering NeuS, followed by inverse
neural radiosity that uses the previously predicted geometry to estimate albedo
and roughness. However, such a naive combination fails and we propose multiple
technical contributions that enable this two-stage approach. We observe that
NeuS fails to handle near-field illumination and strong specular reflections
from the flashlight in a scene. We propose to implicitly model the effects of
near-field illumination and introduce a surface angle loss function to handle
specular reflections. Similarly, we observe that invNeRad assumes constant
illumination throughout the capture and cannot handle moving flashlights during
capture. We propose a light position-aware radiance cache network and
additional smoothness priors on roughness to reconstruct reflectance.
Experimental evaluation on synthetic and real data shows that our method
outperforms the existing co-located light-camera-based inverse rendering
techniques. Our approach produces significantly better reflectance and slightly
better geometry than capture strategies that do not require a dark room.

本研究提出了一种名为 GaNI 的全球和近场照明感知的神经逆渲染技术，能够从光和相机共同拍摄的场景图像中重建几何、反射率和粗糙度参数。我们通过两个阶段的方法来解决现有的逆渲染技术只能针对单个物体的问题，首先利用神经体积渲染 NeuS 重建几何，然后使用先前预测的几何来估计反射率和粗糙度。实验证明，我们的方法在合成和真实数据上表现优于现有的基于光和相机共同拍摄的逆渲染技术，能够产生更好的反射率和略微更好的几何。

GaNI: 全局和近场光照感知的神经反渲染

GaNI: Global and Near Field Illumination Aware Neural Inverse Rendering

In this paper, we propose the Continuous Time Fractional Topic Model (cFTM),
a new method for dynamic topic modeling. This approach incorporates fractional
Brownian motion~(fBm) to effectively identify positive or negative correlations
in topic and word distribution over time, revealing long-term dependency or
roughness. Our theoretical analysis shows that the cFTM can capture these
long-term dependency or roughness in both topic and word distributions,
mirroring the main characteristics of fBm. Moreover, we prove that the
parameter estimation process for the cFTM is on par with that of LDA,
traditional topic models. To demonstrate the cFTM's property, we conduct
empirical study using economic news articles. The results from these tests
support the model's ability to identify and track long-term dependency or
roughness in topics over time.

本文介绍了连续时间分数主题模型（cFTM），这是一种新的动态主题建模方法，它利用分数布朗运动（fBm）有效地识别时间上的主题和词分布的正向或负向相关性，揭示出长期依赖或粗糙性。我们的理论分析证明了 cFTM 可以捕捉主题和词分布中的这种长期依赖或粗糙性，反映了 fBm 的主要特征。此外，我们证明了 cFTM 的参数估计过程与 LDA（传统主题模型）的相当。为了验证 cFTM 的性能，我们使用经济新闻文章进行了实证研究。这些测试的结果支持该模型在跟踪主题的长期依赖或粗糙性方面的能力。

CFTM：连续时间分数主题模型

CFTM: Continuous time fractional topic model

We propose a method to control material attributes of objects like roughness,
metallic, albedo, and transparency in real images. Our method capitalizes on
the generative prior of text-to-image models known for photorealism, employing
a scalar value and instructions to alter low-level material properties.
Addressing the lack of datasets with controlled material attributes, we
generated an object-centric synthetic dataset with physically-based materials.
Fine-tuning a modified pre-trained text-to-image model on this synthetic
dataset enables us to edit material properties in real-world images while
preserving all other attributes. We show the potential application of our model
to material edited NeRFs.

我们提出了一种方法来控制物体的材质属性，如粗糙度、金属性、反照率和透明度。我们的方法利用了文本到图像模型的生成先验知识，通过标量值和指令来改变低级材料属性。我们通过生成基于物理的材料的以物体为中心的合成数据集，解决了缺乏具有受控材质属性的数据集的问题。通过在这个合成数据集上微调修改过的预训练文本到图像模型，我们能够在保留其他所有属性的同时编辑真实世界图像中的材质属性。我们展示了我们的模型在材质编辑 NeRF（可编辑反射函数）中的潜在应用。