We introduce a lightweight and accurate localization method that only
utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map, our approach
localizes a panorama image, taking advantage of the holistic 360 view. The
system mitigates potential privacy breaches or domain discrepancies by avoiding
trained or hand-crafted visual descriptors. However, as lines alone can be
ambiguous, we express distinctive yet compact spatial contexts from
relationships between lines, namely the dominant directions of parallel lines
and the intersection between non-parallel lines. The resulting representations
are efficient in processing time and memory compared to conventional visual
descriptor-based methods. Given the groups of dominant line directions and
their intersections, we accelerate the search process to test thousands of pose
candidates in less than a millisecond without sacrificing accuracy. We
empirically show that the proposed 2D-3D matching can localize panoramas for
challenging scenes with similar structures, dramatic domain shifts or
illumination changes. Our fully geometric approach does not involve extensive
parameter tuning or neural network training, making it a practical algorithm
that can be readily deployed in the real world. Project page including the code
is available through this link: this https URL

介绍了一种利用 2D-3D 线条几何的轻量级而准确的定位方法，通过全景视图来定位，避免了训练或手工制作视觉描述符，提取线条关系中的主导方向和非平行线条的交点等以表达独特而紧凑的空间上下文，其结果表示与传统视觉描述符方法相比，处理时间和内存上更加高效；验证了该方法在具有相似结构、领域转变或光照变化的挑战性场景中的定位效果；由于是完全几何的方法，无需进行庞大的参数调整或神经网络训练，因此具有实际应用价值，可以直接部署在实际世界中。

全几何全景定位

Fully Geometric Panoramic Localization

For any video codecs, the coding efficiency highly relies on whether the
current signal to be encoded can find the relevant contexts from the previous
reconstructed signals. Traditional codec has verified more contexts bring
substantial coding gain, but in a time-consuming manner. However, for the
emerging neural video codec (NVC), its contexts are still limited, leading to
low compression ratio. To boost NVC, this paper proposes increasing the context
diversity in both temporal and spatial dimensions. First, we guide the model to
learn hierarchical quality patterns across frames, which enriches long-term and
yet high-quality temporal contexts. Furthermore, to tap the potential of
optical flow-based coding framework, we introduce a group-based offset
diversity where the cross-group interaction is proposed for better context
mining. In addition, this paper also adopts a quadtree-based partition to
increase spatial context diversity when encoding the latent representation in
parallel. Experiments show that our codec obtains 23.5% bitrate saving over
previous SOTA NVC. Better yet, our codec has surpassed the under-developing
next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in
terms of PSNR. The codes are at this https URL

提出增强神经视频编解码器的上下文多样性，分别从时间和空间维度引入了层次质量模式、基于光流的编码框架以及四叉树分区以增加上下文多样性，实验结果表明，相比现有技术，该编解码器的比特率节省达到 23.5％，并在 PSNR 方面超越了传统编解码器 / ECM 的下一代。

多元背景神经视频压缩

Neural Video Compression with Diverse Contexts

Urban planning refers to the efforts of designing land-use configurations.
Effective urban planning can help to mitigate the operational and social
vulnerability of a urban system, such as high tax, crimes, traffic congestion
and accidents, pollution, depression, and anxiety. Due to the high complexity
of urban systems, such tasks are mostly completed by professional planners.
But, human planners take longer time. The recent advance of deep learning
motivates us to ask: can machines learn at a human capability to automatically
and quickly calculate land-use configuration, so human planners can finally
adjust machine-generated plans for specific needs? To this end, we formulate
the automated urban planning problem into a task of learning to configure
land-uses, given the surrounding spatial contexts. To set up the task, we
define a land-use configuration as a longitude-latitude-channel tensor, where
each channel is a category of POIs and the value of an entry is the number of
POIs. The objective is then to propose an adversarial learning framework that
can automatically generate such tensor for an unplanned area. In particular, we
first characterize the contexts of surrounding areas of an unplanned area by
learning representations from spatial graphs using geographic and human
mobility data. Second, we combine each unplanned area and its surrounding
context representation as a tuple, and categorize all the tuples into positive
(well-planned areas) and negative samples (poorly-planned areas). Third, we
develop an adversarial land-use configuration approach, where the surrounding
context representation is fed into a generator to generate a land-use
configuration, and a discriminator learns to distinguish among positive and
negative samples.

通过深度学习和对空间环境的分析，提出了一种用于自动规划城市用地的敌对学习框架，可以加快城市规划进程并提高规划效率。