仅用普通图像序列自动生成可导航的大规模室内模拟环境。
NVSim: Novel View Synthesis Simulator for Large Scale Indoor Navigation
- 用3D高斯点云重构房间，改进稀疏观测下的视觉瑕疵。
- 构建无网格的可通行性检测算法，生成拓扑导航图。
- 适合机器人路径规划与大规模场景仿真研究者。
我们提出NVSim，一个仅需普通图像序列即可自动构建大规模可导航室内模拟环境的框架，克服了传统3D扫描成本高、扩展性差的问题。方法通过改进3D高斯点云渲染，解决机器人移动数据中稀疏观测楼层的视觉伪影问题；引入楼层感知高斯点云（Floor-Aware Gaussian Splatting），确保地面平面清晰可通行；设计了一种新颖的无网格可通行性检测算法，直接分析渲染视图以构建拓扑图。实验表明，系统可从真实世界数据生成有效的大规模导航图。视频演示见https://youtu.be/tTiIQt6nXC8。
原文摘要 · Abstract (English)
We present NVSim, a framework that automatically constructs large-scale, navigable indoor simulators from only common image sequences, overcoming the cost and scalability limitations of traditional 3D scanning. Our approach adapts 3D Gaussian Splatting to address visual artifacts on sparsely observed floors a common issue in robotic traversal data. We introduce Floor-Aware Gaussian Splatting to ensure a clean, navigable ground plane, and a novel mesh-free traversability checking algorithm that constructs a topological graph by directly analyzing rendered views. We demonstrate our system's ability to generate valid, large-scale navigation graphs from real-world data. A video demonstration is avilable at https://youtu.be/tTiIQt6nXC8
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