用雷达+相机融合实现千米级室外3D高斯建图，定位更准、渲染更稳。
Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments
- 融合雷达点云与多普勒信息，动态物体遮挡更精准。
- 支持异步图像帧全局优化，纹理一致性和新视角合成更真实。
- 采用全局八叉树结构，大幅降低内存占用，适合大规模场景。
我们提出Rad-GS，一个面向千米级室外环境的4D雷达-相机同步定位与建图系统，采用可微分的3D高斯作为空间表示。该方法结合原始雷达点云与多普勒信息，以及几何增强点云，指导同步图像中的动态物体掩码，有效缓解渲染伪影并提升定位精度。同时，利用异步图像帧进行全局优化，增强3D高斯表示的纹理一致性与新视角合成保真度。此外，结合全局八叉树结构与针对性的高斯原语管理策略，显著抑制噪声并大幅降低大规模环境下的内存消耗。大量实验与消融研究证明，Rad-GS性能媲美基于相机或激光雷达的传统3D高斯方法，验证了毫米波雷达在鲁棒室外建图中的可行性。真实世界千米级重建结果进一步展示了其在大规模场景重建中的潜力。
原文摘要 · Abstract (English)
We present Rad-GS, a 4D radar-camera SLAM system designed for kilometer-scale outdoor environments, utilizing 3D Gaussian as a differentiable spatial representation. Rad-GS combines the advantages of raw radar point cloud with Doppler information and geometrically enhanced point cloud to guide dynamic object masking in synchronized images, thereby alleviating rendering artifacts and improving localization accuracy. Additionally, unsynchronized image frames are leveraged to globally refine the 3D Gaussian representation, enhancing texture consistency and novel view synthesis fidelity. Furthermore, the global octree structure coupled with a targeted Gaussian primitive management strategy further suppresses noise and significantly reduces memory consumption in large-scale environments. Extensive experiments and ablation studies demonstrate that Rad-GS achieves performance comparable to traditional 3D Gaussian methods based on camera or LiDAR inputs, highlighting the feasibility of robust outdoor mapping using 4D mmWave radar. Real-world reconstruction at kilometer scale validates the potential of Rad-GS for large-scale scene reconstruction.
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