3D Gaussian Splatting has recently emerged as a highly promising technique
for modeling of static 3D scenes. In contrast to Neural Radiance Fields, it
utilizes efficient rasterization allowing for very fast rendering at
high-quality. However, the storage size is significantly higher, which hinders
practical deployment, e.g.~on resource constrained devices. In this paper, we
introduce a compact scene representation organizing the parameters of 3D
Gaussian Splatting (3DGS) into a 2D grid with local homogeneity, ensuring a
drastic reduction in storage requirements without compromising visual quality
during rendering. Central to our idea is the explicit exploitation of
perceptual redundancies present in natural scenes. In essence, the inherent
nature of a scene allows for numerous permutations of Gaussian parameters to
equivalently represent it. To this end, we propose a novel highly parallel
algorithm that regularly arranges the high-dimensional Gaussian parameters into
a 2D grid while preserving their neighborhood structure. During training, we
further enforce local smoothness between the sorted parameters in the grid. The
uncompressed Gaussians use the same structure as 3DGS, ensuring a seamless
integration with established renderers. Our method achieves a reduction factor
of 8x to 26x in size for complex scenes with no increase in training time,
marking a substantial leap forward in the domain of 3D scene distribution and
consumption. Additional information can be found on our project page:
this https URL

我们提出了一种紧凑的场景表示方法，将三维高斯扩散模型的参数组织成具有局部均匀性的二维网格，从而实现了存储需求的大幅度降低，同时在渲染过程中不影响视觉质量。

通过自组织高斯网格实现紧凑的三维场景表示

Compact 3D Scene Representation via Self-Organizing Gaussian Grids

Document retrieval enables users to find their required documents accurately
and quickly. To satisfy the requirement of retrieval efficiency, prevalent deep
neural methods adopt a representation-based matching paradigm, which saves
online matching time by pre-storing document representations offline. However,
the above paradigm consumes vast local storage space, especially when storing
the document as word-grained representations. To tackle this, we present TGTR,
a Topic-Grained Text Representation-based Model for document retrieval.
Following the representation-based matching paradigm, TGTR stores the document
representations offline to ensure retrieval efficiency, whereas it
significantly reduces the storage requirements by using novel topicgrained
representations rather than traditional word-grained. Experimental results
demonstrate that compared to word-grained baselines, TGTR is consistently
competitive with them on TREC CAR and MS MARCO in terms of retrieval accuracy,
but it requires less than 1/10 of the storage space required by them. Moreover,
TGTR overwhelmingly surpasses global-grained baselines in terms of retrieval
accuracy.

TGTR is a model for document retrieval that reduces storage requirements by using novel topic-grained representations, while maintaining competitive retrieval accuracy compared to word-grained baselines, and surpassing global-grained baselines.

基于主题粒度的文本表示模型用于文档检索

Topic-Grained Text Representation-based Model for Document Retrieval

One of the main difficulties of scaling current localization systems to large
environments is the on-board storage required for the maps. In this paper we
propose to learn to compress the map representation such that it is optimal for
the localization task. As a consequence, higher compression rates can be
achieved without loss of localization accuracy when compared to standard coding
schemes that optimize for reconstruction, thus ignoring the end task. Our
experiments show that it is possible to learn a task-specific compression which
reduces storage requirements by two orders of magnitude over general-purpose
codecs such as WebP without sacrificing performance.

针对现有定位系统在大型环境中需要的内存存储问题，本文提出学习压缩地图表达以达到最优定位任务的目的，实验结果表明可以通过学习任务特定的压缩，将存储要求降低两个数量级，而不降低定位准确性