Online dense mapping of urban scenes forms a fundamental cornerstone for
scene understanding and navigation of autonomous vehicles. Recent advancements
in mapping methods are mainly based on NeRF, whose rendering speed is too slow
to meet online requirements. 3D Gaussian Splatting (3DGS), with its rendering
speed hundreds of times faster than NeRF, holds greater potential in online
dense mapping. However, integrating 3DGS into a street-view dense mapping
framework still faces two challenges, including incomplete reconstruction due
to the absence of geometric information beyond the LiDAR coverage area and
extensive computation for reconstruction in large urban scenes. To this end, we
propose HGS-Mapping, an online dense mapping framework in unbounded large-scale
scenes. To attain complete construction, our framework introduces Hybrid
Gaussian Representation, which models different parts of the entire scene using
Gaussians with distinct properties. Furthermore, we employ a hybrid Gaussian
initialization mechanism and an adaptive update method to achieve high-fidelity
and rapid reconstruction. To the best of our knowledge, we are the first to
integrate Gaussian representation into online dense mapping of urban scenes.
Our approach achieves SOTA reconstruction accuracy while only employing 66%
number of Gaussians, leading to 20% faster reconstruction speed.

提出了一种在无界大规模场景中的在线密集建图框架 HGS-Mapping，它引入了混合高斯表示方法，使用具有不同属性的高斯模型来对整个场景的不同部分建模，并采用混合高斯初始化机制和自适应更新方法实现高保真和快速重建，在线密集建图的同时实现了 SOTA 的重建精度，仅使用了 66% 的高斯模型数量，从而加快了 20% 的重建速度。

HGS-Mapping: 基于混合高斯表示的城市场景在线稠密地图构建

HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation  in Urban Scenes

We present GauStudio, a novel modular framework for modeling 3D Gaussian
Splatting (3DGS) to provide standardized, plug-and-play components for users to
easily customize and implement a 3DGS pipeline. Supported by our framework, we
propose a hybrid Gaussian representation with foreground and skyball background
models. Experiments demonstrate this representation reduces artifacts in
unbounded outdoor scenes and improves novel view synthesis. Finally, we propose
Gaussian Splatting Surface Reconstruction (GauS), a novel render-then-fuse
approach for high-fidelity mesh reconstruction from 3DGS inputs without
fine-tuning. Overall, our GauStudio framework, hybrid representation, and GauS
approach enhance 3DGS modeling and rendering capabilities, enabling
higher-quality novel view synthesis and surface reconstruction.

我们介绍了 GauStudio，一个新颖的模块化框架，用于建模 3D 高斯飞溅（3DGS），为用户提供标准化的即插即用组件，以便轻松定制和实现 3DGS 流程。支持我们的框架，我们提出了一种具有前景和天空球背景模型的混合高斯表示方法。实验证明，这种表示方法减少了无边界户外场景中的伪影，并改善了新颖视角合成。最后，我们提出了高斯飞溅表面重建（GauS）方法，它是一种从 3DGS 输入中进行高保真网格重建的新的先渲染再融合的方法，无需微调。总的来说，我们的 GauStudio 框架，混合表示方法和 GauS 方法增强了 3DGS 建模和渲染能力，实现了更高质量的新视角合成和表面重建。