The field of novel-view synthesis has recently witnessed the emergence of 3D
Gaussian Splatting, which represents scenes in a point-based manner and renders
through rasterization. This methodology, in contrast to Radiance Fields that
rely on ray tracing, demonstrates superior rendering quality and speed.
However, the explicit and unstructured nature of 3D Gaussians poses a
significant storage challenge, impeding its broader application. To address
this challenge, we introduce the Gaussian-Forest modeling framework, which
hierarchically represents a scene as a forest of hybrid 3D Gaussians. Each
hybrid Gaussian retains its unique explicit attributes while sharing implicit
ones with its sibling Gaussians, thus optimizing parameterization with
significantly fewer variables. Moreover, adaptive growth and pruning strategies
are designed, ensuring detailed representation in complex regions and a notable
reduction in the number of required Gaussians. Extensive experiments
demonstrate that Gaussian-Forest not only maintains comparable speed and
quality but also achieves a compression rate surpassing 10 times, marking a
significant advancement in efficient scene modeling. Codes are available at
this https URL

介绍了高斯森林建模框架，该框架通过在复杂区域中进行详细表示并显著减少所需的高斯模型数量，从而实现了与现有方法相媲美的速度和质量，并且在压缩比方面取得了 10 倍以上的显著进展。

高斯森林：压缩场景建模的分层混合 3D 高斯飞溅

Gaussian-Forest: Hierarchical-Hybrid 3D Gaussian Splatting for  Compressed Scene Modeling

We address the problem of generating a 360-degree image from a single image
with a narrow field of view by estimating its surroundings. Previous methods
suffered from overfitting to the training resolution and deterministic
generation. This paper proposes a completion method using a transformer for
scene modeling and novel methods to improve the properties of a 360-degree
image on the output image. Specifically, we use CompletionNets with a
transformer to perform diverse completions and AdjustmentNet to match color,
stitching, and resolution with an input image, enabling inference at any
resolution. To improve the properties of a 360-degree image on an output image,
we also propose WS-perceptual loss and circular inference. Thorough experiments
show that our method outperforms state-of-the-art (SOTA) methods both
qualitatively and quantitatively. For example, compared to SOTA methods, our
method completes images 16 times larger in resolution and achieves 1.7 times
lower Frechet inception distance (FID). Furthermore, we propose a pipeline that
uses the completion results for lighting and background of 3DCG scenes. Our
plausible background completion enables perceptually natural results in the
application of inserting virtual objects with specular surfaces.

本文提出了基于 Transformers 和 CompletionNets 的自动场景建模方法，使用 WS-perceptual loss 和 circular inference 提升 360 度图像的质量，并在虚拟物体插入场景上取得了具有感知自然性的结果。

高效 3DCG 背景创建的多样真实 360 度图像外延技术

Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG  Background Creation

In this work, we tackle the problem of active camera localization, which
controls the camera movements actively to achieve an accurate camera pose. The
past solutions are mostly based on Markov Localization, which reduces the
position-wise camera uncertainty for localization. These approaches localize
the camera in the discrete pose space and are agnostic to the
localization-driven scene property, which restricts the camera pose accuracy in
the coarse scale. We propose to overcome these limitations via a novel active
camera localization algorithm, composed of a passive and an active localization
module. The former optimizes the camera pose in the continuous pose space by
establishing point-wise camera-world correspondences. The latter explicitly
models the scene and camera uncertainty components to plan the right path for
accurate camera pose estimation. We validate our algorithm on the challenging
localization scenarios from both synthetic and scanned real-world indoor
scenes. Experimental results demonstrate that our algorithm outperforms both
the state-of-the-art Markov Localization based approach and other compared
approaches on the fine-scale camera pose accuracy. Code and data are released
at this https URL

提出一种新型的主动摄像机定位算法，包括一个被动和一个主动模块，前者通过建立点对点的相机与世界的对应关系，在连续的位姿空间中优化相机位姿，后者则显式地建模场景和相机的不确定性部件以规划精准摄像机位姿估计的路径。