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.
在线密集映射城市场景构成了自动驾驶车辆场景理解和导航的基本基石。最近在映射方法上的进步主要基于NeRF，其渲染速度太慢，无法满足在线要求。三维高斯喷涂（3DGS），由于其渲染速度比NeRF快数百倍，因此在在线密集映射中拥有更大的潜力。然而，将3DGS整合到街景密集映射框架中仍面临两大挑战，包括由于缺乏LiDAR覆盖区域之外的几何信息而导致的重建不完整，以及在大型城市场景中进行重建的大量计算。为此，我们提出了HGS-Mapping，一个在无边界大规模场景中的在线密集映射框架。为了实现完整构建，我们的框架引入了混合高斯表示法，该方法使用具有不同属性的高斯模型来模拟整个场景的不同部分。此外，我们采用混合高斯初始化机制和自适应更新方法来实现高保真和快速重建。据我们所知，我们是第一个将高斯表示法整合到城市场景在线密集映射中的。我们的方法在仅使用66%的高斯数量的情况下，实现了SOTA的重建精度，重建速度提高了20%。