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

arXiv: https://arxiv.org/abs/2603.22650 · HTML: https://mlsanity.com/paper/2603.22650/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 49 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 30 of 30)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [ActiveNeRF: Learning where to See with Uncertainty Estimation](https://mlsanity.com/paper/2209.08546/) (2022)
- [RaDe-GS: Rasterizing Depth in Gaussian Splatting](https://mlsanity.com/paper/2406.01467/) (2024)
- [Occupancy Anticipation for Efficient Exploration and Navigation](https://mlsanity.com/paper/2008.09285/) (2020)
- [Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields](https://mlsanity.com/paper/2209.08409/) (2022)
- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [MILo: Mesh-In-the-Loop Gaussian Splatting for Detailed and Efficient Surface Reconstruction](https://mlsanity.com/paper/2506.24096/) (2025)
- [ActiveGS: Active Scene Reconstruction Using Gaussian Splatting](https://mlsanity.com/paper/2412.17769/) (2024)
- [Uncertainty-driven Planner for Exploration and Navigation](https://mlsanity.com/paper/2202.11907/) (2022)
- [ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting](https://mlsanity.com/paper/2410.21955/) (2024)
- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [NARUTO: Neural Active Reconstruction from Uncertain Target Observations](https://mlsanity.com/paper/2402.18771/) (2024)
- [Supervised Learning of the Next-Best-View for 3D Object Reconstruction](https://mlsanity.com/paper/1905.05833/) (2019)
- [Fast Frontier-based Information-driven Autonomous Exploration with an MAV](https://mlsanity.com/paper/2002.04440/) (2020)
- [MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervision](https://mlsanity.com/paper/2303.03315/) (2023)
- [ActiveGAMER: Active GAussian Mapping through Efficient Rendering](https://mlsanity.com/paper/2501.06897/) (2025)
- [SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration](https://mlsanity.com/paper/2208.10449/) (2022)
- [Submodular Trajectory Optimization for Aerial 3D Scanning](https://mlsanity.com/paper/1705.00703/) (2017)
- [NextBestPath: Efficient 3D Mapping of Unseen Environments](https://mlsanity.com/paper/2502.05378/) (2025)
- [A Multi-Resolution Frontier-Based Planner for Autonomous 3D Exploration](https://mlsanity.com/paper/2011.02182/) (2020)
- [GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes](https://mlsanity.com/paper/2505.20294/) (2025)
- [Understanding while Exploring: Semantics-driven Active Mapping](https://mlsanity.com/paper/2506.00225/) (2025)

## Built on by (most cited first, 5 of 5)

- [R2S-EGO: Dual-Proxy Refinement for Sparse-Capture Real-to-Sim](https://mlsanity.com/paper/2608.06827/) (2026)
- [Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration](https://mlsanity.com/paper/2605.22814/) (2026)
- [Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping](https://mlsanity.com/paper/2609.06820/) (2026)
- [Rethinking Learned Occupancy in Autonomous Active Mapping with Observation-Gated Filtering](https://mlsanity.com/paper/2609.09069/) (2026)
- [Matisse: Evidence-Space Reasoning for Active 3D Reconstruction](https://mlsanity.com/paper/2609.38746/) (2026)

## Abstract (arXiv)

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.