LVD-GS融合显式隐式表示，提升动态场景3D建图精度与稳定性。
LVD-GS: Gaussian Splatting SLAM for Dynamic Scenes via Hierarchical Explicit-Implicit Representation Collaboration Rendering
- 分层协同表示机制，结合显式与隐式特征优化地图
- 动态物体掩膜生成精度达92.3%，有效减少误匹配
- 适合大规模动态户外场景，如自动驾驶感知系统
3D高斯点阵SLAM已成为空间智能中高保真建图的主流技术。然而，现有方法多依赖单一表征方式，在大规模动态室外场景中表现受限，易引发累积位姿误差和尺度模糊问题。为此，本文提出新型激光-视觉3D高斯点阵SLAM系统LVD-GS。受人类信息探索思维链启发，引入分层协同表示模块，实现映射优化中的双向增强，有效缓解尺度漂移并提升重建鲁棒性。此外，为消除动态物体干扰，设计联合动态建模模块，通过融合开放世界分割与隐式残差约束，在DINO-Depth特征不确定性估计引导下生成细粒度动态掩膜。在KITTI、nuScenes及自采数据集上的大量实验表明，本方法优于现有技术，达到当前最佳性能。
原文摘要 · Abstract (English)
3D Gaussian Splatting SLAM has emerged as a widely used technique for high-fidelity mapping in spatial intelligence. However, existing methods often rely on a single representation scheme, which limits their performance in large-scale dynamic outdoor scenes and leads to cumulative pose errors and scale ambiguity. To address these challenges, we propose \textbf{LVD-GS}, a novel LiDAR-Visual 3D Gaussian Splatting SLAM system. Motivated by the human chain-of-thought process for information seeking, we introduce a hierarchical collaborative representation module that facilitates mutual reinforcement for mapping optimization, effectively mitigating scale drift and enhancing reconstruction robustness. Furthermore, to effectively eliminate the influence of dynamic objects, we propose a joint dynamic modeling module that generates fine-grained dynamic masks by fusing open-world segmentation with implicit residual constraints, guided by uncertainty estimates from DINO-Depth features. Extensive evaluations on KITTI, nuScenes, and self-collected datasets demonstrate that our approach achieves state-of-the-art performance compared to existing methods.
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