examples/
└── kitti_00/
 ├── robot_01/
 │ ├── kf_000000/
 │ │ ├── image.png
 │ │ ├── pose_4x4.npy # Sim(3) pose (×ばつ4, scale encoded in rotation)
 │ │ ├── pointcloud_local.npz # keys: xyz (N,3), colors (N,3) uint8
 │ │ └── pose_tum.txt # timestamp tx ty tz qx qy qz qw
 │ └── kf_000001/ ...
 ├── robot_02/ ...
 └── robot_03/ ...

device: "cuda:0"
paths:
 mast3r_config: "./MASt3R-SLAM/config/base.yaml"
 model_weights: "./MASt3R-SLAM/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth"
 retriever_weights: "./MASt3R-SLAM/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth"
 loop_vis_save_dir: "./MASt3R-SLAM/retrieval_test/loop_closures"
image:
 height: 384
 width: 512
# noise values are 1-sigma: information = 1 / sigma^2
# rotation unit: degrees, translation unit: meters
graph:
 odometry:
 t_noise: [0.1, 0.1, 0.1] # translation sigma (m)
 r_noise: [5.0, 5.0, 5.0] # rotation sigma (deg)
 s_noise: 0.05 # scale sigma
 loop:
 t_noise: [1.0, 1.0, 1.0]
 r_noise: [25.0, 25.0, 25.0]
 s_noise: 1.0
 loop_inhibit_window: 5 # frames to suppress repeated loop edges between the same pair
 same_robot_min_seq_gap: 90 # minimum seq-index gap for same-robot loop closure
matching:
 retrieval_k: 3 # top-k candidates from image retrieval
 retrieval_min_thresh: 0.025 # minimum retrieval score to consider a candidate
 quality_threshold: 1.5 # Qk threshold for valid 3-D correspondences
 match_frac_min: 0.1 # minimum fraction of valid matches to attempt Sim3
point_cloud:
 local_voxel_size: 0.3 # voxel size for local map downsampling (m)
 pre_slice_rate: 10 # stride applied before voxel downsampling
 global_voxel_size: 0.3 # voxel size for global map rebuild after optimization (m)
anchor:
 scale_min: 0.5 # reject child anchor if scale < this
 scale_max: 4.0 # reject child anchor if scale > this
optimization:
 stage1_iters: 10
 stage2_iters: 50
 verbose: 0
vis:
 point_radii: 0.15 # rerun point cloud radius
 trajectory_radii: 0.15 # rerun trajectory line radius

MR.ScaleMaster/
├── main.py # Entry point
├── requirements.txt
│
├── scripts/
│ ├── run.sh # Launch script (activates venv, sets PYTHONPATH)
│ ├── setup.bash # One-time environment & build setup
│ ├── download_checkpoint.sh # Model checkpoint downloader
│ ├── install_loger.sh # LoGeR front-end setup (optional)
│ ├── do_collaborative_mapping.sh # Try Your Own Data! pipeline
│ └── demo_viser_for_mrscalemaster.py
│
├── config/
│ ├── Exp1.yaml # Config for Exp1
│ ├── Exp2.yaml # Config for Exp2
│ └── KITTI.yaml # Config for KITTI sequences
│
├── cores/ # Main Python package
│ ├── dataloader.py # Dataset scanner & Qt data loader thread
│ ├── slam_backend.py # MASt3R inference + Sim(3) loop detection
│ ├── optimizer_worker.py # g2o optimization thread
│ ├── inference_thread.py # Per-robot inference & map update
│ ├── retrieval.py # Image retrieval for loop candidates
│ ├── visualizer.py # Rerun-based 3D visualization
│ ├── config.py # Colors, timing utilities
│ ├── math_utils.py
│ ├── io_utils.py
│ └── gui/
│ ├── monitor_window.py # Main Qt window
│ └── robot_panel.py # Per-robot status panel
│
└── cpp/ # C++ Sim(3) optimizer (pybind11)
 ├── build.sh
 ├── CMakeLists.txt
 ├── cmake/
 ├── src/
 ├── include/
 └── thirdparty/
 ├── g2o/ # git submodule
 └── cnpy/ # git submodule

@inproceedings{mast3rslam,
 title = {MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors},
 author = {Murai, Riku and others},
 booktitle = {CVPR},
 year = {2025}
}
@article{maggio2026vggtslam2,
 title = {VGGT-SLAM 2.0: Real-time Dense Feed-forward Scene Reconstruction},
 author = {Maggio, Dominic and Carlone, Luca},
 journal = {arXiv preprint arXiv:2601.19887},
 year = {2026}
}
@article{wang2025pi3,
 title = {$\pi^3$: Permutation-Equivariant Visual Geometry Learning},
 author = {Wang, Yifan and Zhou, Jianjun and Zhu, Haoyi and others},
 journal = {arXiv preprint arXiv:2507.13347},
 year = {2025}
}
@article{zhang2026loger,
 title = {LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory},
 author = {Zhang, Junyi and Herrmann, Charles and Hur, Junhwa and Sun, Chen and Yang, Ming-Hsuan and Cole, Forrester and Darrell, Trevor and Sun, Deqing},
 journal = {arXiv preprint arXiv:2603.03269},
 year = {2026}
}