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.

翻译：主动建图旨在确定智能体应如何移动以高效重建未知环境。现有方法大多依赖贪婪的下一最佳视角预测，导致探索效率低下且重建不完整。为解决此问题，我们提出MAGICIAN，一种创新的长期规划框架，通过想象高斯（一种基于3D高斯泼溅的场景表征，源自具有强结构先验的预训练占据网络）最大化累积表面覆盖增益。该表征通过快速体渲染实现任意新视角的高效覆盖增益计算，从而可集成到树搜索算法中进行长时域规划。我们以闭环方式更新想象高斯并优化轨迹。本方法在具有不同动作空间的室内外基准测试中均实现了最先进性能，凸显了长期规划在主动建图中的优势。