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

Shiyao Li, Antoine Guédon, Shizhe Chen, Vincent Lepetit

Getting Started

1. Clone the Repository

git clone --recursive git@github.com:shiyao-li/MAGICIAN.git
cd MAGICIAN

2. Environment Setup

conda env create -f environment.yml
conda activate magician
cd RaDe-GS
pip install -r requirements.txt
pip install submodules/diff-gaussian-rasterization --no-build-isolation
pip install submodules/warp-patch-ncc --no-build-isolation
pip install submodules/simple-knn/ --no-build-isolation
pip install git+https://github.com/rahul-goel/fused-ssim/ --no-build-isolation

# tetra-nerf for Marching Tetrahedra
conda install conda-forge::cgal
pip install submodules/tetra_triangulation/ --no-build-isolation

3. Dataset

Download the dataset here and place it under a data/ folder in the project root.

4. Pretrained Weights

Download the pretrained model weights from Google Drive and place them under a weights/ folder in the project root.

5. Run

python test_magician_planning.py

6. Configuration

Key parameters are in configs/test/test_in_default_scenes_config.json:

Parameter	Description
beam_width	Beam search: number of candidates kept at each step
beam_steps	Beam search: lookahead depth (number of steps)
lmdb_dir_name	Name of the output LMDB directory under results/scene_exploration/

7. Evaluate Metrics

python evaluation_lmdb.py

The LMDB file (specified by lmdb_dir_name) stores the following data for each trajectory:

• Coverage update history: how coverage evolves step by step
• Camera poses: the full history of visited camera positions
• Final point cloud: the reconstructed point cloud at the end of the trajectory

Citation

@inproceedings{li2026magician,
  author = "Shiyao Li and Antoine Guédon and Shizhe Chen and Vincent Lepetit",
  title = {{MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping}},
  booktitle = {{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
  year = 2026
}