MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping

Active mapping aims to determine how an agent should move to efficiently reconstruct an unknown environment. Most existing approaches rely on greedy next-best-view prediction, resulting in inefficient exploration and incomplete scene reconstruction.To address this limitation, we introduce MAGICIAN a novel long-term planning framework that maximizes accumulated surface coverage gain through Imagined Gaussians, a predicted scene representation derived from a pre-trained occupancy network with strong structural priors. This representation enables efficient computation of overage gain for any novel viewpoint via fast volumetric rendering.The resulting speedup allows the integration of the gain metric into a tree-search algorithm for planning long-horizon paths.We update Imagined Gaussians and refine the planned trajectory in a closed-loop manner.Our method achieves state-of-the-art performance across indoor and outdoor benchmarks with varying action spaces, demonstrating the critical advantage of long-term planning in active mapping.