Scene stylization extends the work of neural style transfer to three spatial
dimensions. A vital challenge in this problem is to maintain the uniformity of
the stylized appearance across a multi-view setting. A vast majority of the
previous works achieve this by optimizing the scene with a specific style
image. In contrast, we propose a novel architecture trained on a collection of
style images, that at test time produces high quality stylized novel views. Our
work builds up on the framework of 3D Gaussian splatting. For a given scene, we
take the pretrained Gaussians and process them using a multi resolution hash
grid and a tiny MLP to obtain the conditional stylised views. The explicit
nature of 3D Gaussians give us inherent advantages over NeRF-based methods
including geometric consistency, along with having a fast training and
rendering regime. This enables our method to be useful for vast practical use
cases such as in augmented or virtual reality applications. Through our
experiments, we show our methods achieve state-of-the-art performance with
superior visual quality on various indoor and outdoor real-world data.

使用三维高斯喷洒方法进行场景风格化研究，通过在多视角环境中保持一致的风格外观来产生高质量的风格化新视角，实现了在增强现实或虚拟现实应用中具有卓越视觉质量的最新表现。

高斯飞溅式生成

Gaussian Splatting in Style

Synthesizing novel views from a single view image is a highly ill-posed
problem. We discover an effective solution to reduce the learning ambiguity by
expanding the single-view view synthesis problem to a multi-view setting.
Specifically, we leverage the reliable and explicit stereo prior to generate a
pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D
space. In this way, the challenging novel view synthesis process is decoupled
into two simpler problems of stereo synthesis and 3D reconstruction. In order
to synthesize a structurally correct and detail-preserved stereo image, we
propose a self-rectified stereo synthesis to amend erroneous regions in an
identify-rectify manner. Hard-to-train and incorrect warping samples are first
discovered by two strategies, 1) pruning the network to reveal low-confident
predictions; and 2) bidirectionally matching between stereo images to allow the
discovery of improper mapping. These regions are then inpainted to form the
final pseudo-stereo. With the aid of this extra input, a preferable 3D
reconstruction can be easily obtained, and our method can work with arbitrary
3D representations. Extensive experiments show that our method outperforms
state-of-the-art single-view view synthesis methods and stereo synthesis
methods.

通过将单视图合成问题扩展到多视图设置并利用可靠的和显式的立体先验来生成伪立体视角解决了学习问题中的模糊性，将具有挑战性的新颖视图合成过程分解为两个简单的问题：立体合成和三维重建。

自校正伪立体视图合成的单视角生成

Single-View View Synthesis with Self-Rectified Pseudo-Stereo

Self-supervised learning is an empirically successful approach to
unsupervised learning based on creating artificial supervised learning
problems. A popular self-supervised approach to representation learning is
contrastive learning, which leverages naturally occurring pairs of similar and
dissimilar data points, or multiple views of the same data. This work provides
a theoretical analysis of contrastive learning in the multi-view setting, where
two views of each datum are available. The main result is that linear functions
of the learned representations are nearly optimal on downstream prediction
tasks whenever the two views provide redundant information about the label.

本文针对多视图环境下对比学习进行理论分析，结果表明当视图提供有关标签的冗余信息时，所学习的表示的线性函数在下游预测任务中几乎是最优的。

对比学习，多视角冗余和线性模型

Contrastive learning, multi-view redundancy, and linear models

Despite the promising progress made in recent years, person re-identification
remains a challenging task due to complex variations in human appearances from
different camera views. This paper presents a logistic discriminant metric
learning method for this challenging problem. Different with most existing
metric learning algorithms, it exploits both original data and auxiliary data
during training, which is motivated by the new machine learning paradigm -
Learning Using Privileged Information. Such privileged information is a kind of
auxiliary knowledge which is only available during training. Our goal is to
learn an optimal distance function by constructing a locally adaptive decision
rule with the help of privileged information. We jointly learn two distance
metrics by minimizing the empirical loss penalizing the difference between the
distance in the original space and that in the privileged space. In our
setting, the distance in the privileged space functions as a local decision
threshold, which guides the decision making in the original space like a
teacher. The metric learned from the original space is used to compute the
distance between a probe image and a gallery image during testing. In addition,
we extend the proposed approach to a multi-view setting which is able to
explore the complementation of multiple feature representations. In the
multi-view setting, multiple metrics corresponding to different original
features are jointly learned, guided by the same privileged information.
Besides, an effective iterative optimization scheme is introduced to
simultaneously optimize the metrics and the assigned metric weights. Experiment
results on several widely-used datasets demonstrate that the proposed approach
is superior to global decision threshold based methods and outperforms most
state-of-the-art results.

本文介绍了一种基于逻辑判别度量学习算法，能够在人物视角发生大量变化的情况下实现人物重新识别，通过在训练期间利用辅助信息同时利用原始数据和辅助数据来完成度量学习，可以用于单视角和多视角任务