用平面高斯点云实现城市道路大场景高效重建
StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting
- 基于平面八叉树与分段训练降低内存占用
- 多尺度匹配+引导平滑，解决遮挡与稀疏视图问题
- 适合自动驾驶与城市规划的大场景重建任务
城市道路场景重建对自动驾驶和城市规划至关重要。这类场景具有长而狭窄的相机轨迹、遮挡严重、物体关系复杂以及多尺度数据稀疏等特点。现有以物体为中心的表面重建方法难以适应此类特性。为此，我们提出StreetSurfGS，首个专为可扩展城市街景表面重建设计的高斯点云方法。该方法采用基于平面的八叉树表示与分段训练，降低内存开销，适应独特相机轨迹并保证可扩展性。针对物体重叠导致的深度误差，提出正则化内的引导平滑策略，消除边界点与离群点。为应对视图稀疏与多尺度挑战，引入双阶段匹配策略，融合邻近与长期信息。大量实验验证了StreetSurfGS在新视角合成与表面重建上的有效性。
原文摘要 · Abstract (English)
Reconstructing urban street scenes is crucial due to its vital role in applications such as autonomous driving and urban planning. These scenes are characterized by long and narrow camera trajectories, occlusion, complex object relationships, and data sparsity across multiple scales. Despite recent advancements, existing surface reconstruction methods, which are primarily designed for object-centric scenarios, struggle to adapt effectively to the unique characteristics of street scenes. To address this challenge, we introduce StreetSurfGS, the first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction. StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability. Additionally, to mitigate depth inaccuracies caused by object overlap, we propose a guided smoothing strategy within regularization to eliminate inaccurate boundary points and outliers. Furthermore, to address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information. Extensive experiments validate the efficacy of StreetSurfGS in both novel view synthesis and surface reconstruction.
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