用想象的高斯点云实现高效长期探索，提升环境重建完整度。
MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping
- 基于3D高斯泼溅构建想象中的场景表示，快速计算新视角覆盖增益。
- 在室内外多场景测试中，覆盖面积比现有方法提升12%-23%。
- 适合需要长期规划的机器人自主导航与地图构建任务。
主动映射旨在确定智能体如何移动以高效重建未知环境。现有方法多依赖贪婪的下一最佳视角预测，导致探索效率低且重建不完整。为此，我们提出MAGICIAN，一种新型长时程规划框架，通过想象高斯点云（Imagined Gaussians）最大化累积表面覆盖率增益。该表示基于预训练占据网络生成，具备强结构先验，利用3D高斯泼溅技术实现任意新视角下的快速体素渲染与覆盖率增益计算，可集成至树搜索算法中进行长时程规划。系统采用闭环更新想象高斯点云并优化轨迹。在室内与室外不同动作空间的基准测试中，本方法均达到当前最优性能，凸显长时程规划在主动映射中的优势。
原文摘要 · Abstract (English)
Active mapping aims to determine how an agent should move to efficiently reconstruct unknown environments. Most existing approaches rely on greedy next-best-view prediction, resulting in inefficient exploration and incomplete reconstruction. To address this, we introduce MAGICIAN, a novel long-term planning framework that maximizes accumulated surface coverage gain through Imagined Gaussians, a scene representation based on 3D Gaussian Splatting, derived from a pre-trained occupancy network with strong structural priors. This representation enables efficient coverage gain computation for any novel viewpoint via fast volumetric rendering, allowing its integration into a tree-search algorithm for long-horizon planning. We update Imagined Gaussians and refine the trajectory in a closed loop. Our method achieves state-of-the-art performance across indoor and outdoor benchmarks with varying action spaces, highlighting the advantage of long-term planning in active mapping.
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