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name: mast3r-slam description: "Guides MASt3R-SLAM installation, CUDA backend setup, visual SLAM runs on video/live/dataset inputs, and benchmark evaluation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MASt3R-SLAM

Use this repo skill when the task is about the MASt3R-SLAM project: installing its CUDA-backed package, preparing checkpoints, running dense monocular SLAM on videos, RGB folders, live cameras, or supported benchmark datasets, or planning TUM/7-Scenes/EuRoC/ETH3D evaluations.

MASt3R-SLAM is not a CPU-only utility. The primary runtime imports the custom mast3r_slam_backends extension, uses PyTorch CUDA tensors, and main.py selects cuda:0. Treat CPU-only checks as helper validation only; do not claim a full SLAM run is ready until a CUDA build and CUDA smoke check pass.

Start here

1. Read references/repo-provenance.md when you need to know whether this skill matches the current checkout or should be refreshed.
2. For a new machine or broken install, load setup-and-backends.
3. For a concrete run on an input video, RGB folder, benchmark sequence, RealSense stream, or webcam, load run-slam.
4. For benchmark-suite loops, dataset manifests, result logs, or evo_ape metrics, load evaluation.
5. Use references/overview.md for a quick architecture and workflow map, and references/troubleshooting.md for cross-cutting failure routing.

Minimal verified environment shape

A working MASt3R-SLAM environment needs all of these classes of dependencies:

• Python 3.11 with PyTorch 2.5.1 on a CUDA build matching the driver.
• A CUDA build toolkit, not only a runtime. nvcc and cuda_runtime.h must be visible when the root package compiles mast3r_slam_backends.
• Populated submodules for Eigen and pyimgui before installing thirdparty/in3d and compiling the root extension.
• Editable installs for the vendored thirdparty/mast3r, thirdparty/in3d, and then the root package with build isolation disabled for extension builds.
• MASt3R metric and retrieval checkpoints before any real SLAM run.

After installation, run the bundled diagnostic rather than guessing:

python scripts/check_install.py --check-cuda

Add --checkpoint-dir <path> to validate checkpoint filenames and --repo-root <path-to-MASt3R-SLAM-checkout> when the package is not already installed in the active Python environment.

Route map

Need	Read
Install order, CUDA/PyTorch/nvcc choices, submodules, checkpoints, import checks	setup-and-backends
Build a safe command for main.py, choose --no-viz, --calib, --save-as, or generate config templates	run-slam
Validate TUM/EuRoC/ETH3D/7-Scenes/RGB-folder/MP4/webcam/RealSense input layout	run-slam references/data-formats.md
Prepare benchmark dataset download plans without immediately downloading large archives	evaluation
Recreate eval_*.sh behavior, print or run suite commands, compute evo_ape metrics	evaluation references/evaluation-workflows.md
Diagnose CUDA not found, missing mast3r_slam_backends, OpenGL/RealSense, checkpoints, missing logs, or calibration variants	references/troubleshooting.md first, then the owning sub-skill troubleshooting file

Operating constraints

• Do not run dataset download or checkpoint download commands implicitly; they are network and storage side effects. Use the bundled planners to print the exact commands and ask before executing them.
• Do not substitute a CPU import, main.py --help, or config parser check for a full SLAM backend verification. Those are useful helper checks only.
• Do not rely on upstream README/script/config files being opened during future tasks. This skill bundles the relevant install sequence, config templates, dataset manifests, and command planners.
• Do not use vendored MASt3R/Dust3R demos or training entry points as MASt3R-SLAM workflows unless the user explicitly asks for upstream MASt3R internals; this repo skill focuses on the SLAM package.

Quick task recipes

• Install failed: load setup-and-backends, then run scripts/check_install.py --check-cuda and follow the symptom-specific troubleshooting notes.
• Run one video or RGB folder: load run-slam, generate or select a config, validate the input layout, and use run_mast3r_slam.py --dry-run before any long execution.
• Run a benchmark suite: load evaluation, print the suite commands with plan_evaluation.py, confirm datasets/checkpoints/log destinations, then run only the approved sequences.
• Compute metrics only: use evaluation metric-only guidance; do not rerun SLAM when the needed trajectory files already exist.