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

Abstract

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 scene representation derived from a pre-trained occupancy network with strong structural priors. This representation enables efficient computation of coverage gain 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 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.

Figures (11)

• Figure 1: MAGICIAN enables efficient, high-coverage exploration across diverse environments. We visualize the exploration trajectories (light-to-dark gradients) generated by our method and the resulting 3D reconstructions (surface meshes and textures) for various outdoor and indoor scenes. MAGICIAN is powered by what we call "Imagined Gaussians", predicted by our occupancy model to model scene uncertainty, making efficient long-term planning possible.
• Figure 2: Overview of the proposed MAGICIAN framework. At time $t$, we first predict the occupancy field using the occupancy model and update the Imagined Gaussians. We can then efficiently estimate the coverage gain and apply beam search to plan $N_b$ candidate trajectories, selecting the one with the highest expected gain. The agent then executes the first $N_f$ actions of the best trajectory $\tau_k$ of length $N_d$ before repeating this process in the next planning loop. In this figure, lighter colors in the Imagined Gaussians indicate higher novelty, while darker colors correspond to previously observed areas. The first trajectory darkens the novelty field the most, representing the optimal path at time $t$.
• Figure 3: Computing coverage gain with Imagined Gaussians. During beam search, we evaluate candidate poses by rendering novelty maps from the Imagined Gaussians to compute the coverage gain. The corresponding depth maps are then used to update the novelty ${\hat{\gamma}}$ of Gaussians within a depth tolerance $\epsilon_d$.
• Figure 4: Evolution of Imagined Gaussians Compared with Ground Truth Mesh. The brighter the Gaussians, the higher their predicted occupancy. As exploration progresses (from left to right), our Imagined Gaussians increasingly align with the ground truth mesh, demonstrating improved environmental modeling.
• Figure 5: 3D reconstructions obtained with our trajectories. We show Gaussian splatting renderings (top row) and normal maps of the reconstructed meshes (bottom row) after applying Mesh-In-the-Loop Gaussian Splatting guedon2025milo on 100 RGB images collected along our trajectories. The trajectories output by our method cover the entire scene surfaces, resulting in complete and accurate surface meshes.