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name: mono-gs description: "Use MonoGS for CUDA Gaussian-splatting SLAM, dataset/config setup, offline evaluation, and RealSense live demos." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MonoGS

Use this repo skill when a task is about MonoGS / Gaussian Splatting SLAM: installing the runtime, preparing datasets/configs, running monocular/RGB-D/stereo SLAM, evaluating results, or operating the RealSense live GUI demo.

MonoGS is CUDA-first. Core SLAM and evaluation workflows require CUDA-capable PyTorch plus the simple_knn and diff_gaussian_rasterization native extensions; a CPU-only environment is not a valid substitute for those workflows.

Quick route map

User intent	Read
Install MonoGS, build submodules, verify CUDA/backend imports	environment-setup
Download/validate TUM, Replica, EuRoC data or edit YAML configs	data-and-configs
Run offline monocular, RGB-D, or stereo SLAM with slam.py	offline-slam
Use --eval, inspect result folders, ATE/render metrics, W&B behavior	evaluation-and-results
Run/debug live Intel RealSense capture or the Open3D/OpenGL GUI	live-demo

Minimal install shape

From a MonoGS checkout, the documented baseline is a Conda environment with Python 3.7, PyTorch 1.12.1, CUDA 11.6, Open3D, evo, W&B, GUI dependencies, and the two recursive submodules built as editable CUDA extensions. Read environment-setup before installing; that sub-skill has the build order and troubleshooting gates.

After installation, run the bundled diagnostic:

python scripts/check_monogs_environment.py --repo-root <mono-gs-checkout> --require-cuda

Main commands

# Monocular TUM
python slam.py --config configs/mono/tum/fr3_office.yaml

# RGB-D TUM
python slam.py --config configs/rgbd/tum/fr3_office.yaml

# RGB-D Replica single-process-style config
python slam.py --config configs/rgbd/replica/office0_sp.yaml

# Stereo EuRoC
python slam.py --config configs/stereo/euroc/mh02.yaml

# Headless evaluation with W&B disabled by environment
WANDB_MODE=disabled python slam.py --config configs/mono/tum/fr3_office.yaml --eval

Validate dataset roots before launching long runs with the helper in data-and-configs, and use the planner in offline-slam when constructing commands for custom configs.

Shared references and scripts

• Architecture maps the CLI, config loader, dataset wrappers, frontend/backend, GUI, and evaluation flow.
• Troubleshooting routes common failures to the right sub-skill.
• Repository provenance records the source snapshot used to build this skill; read it before deciding whether a checkout needs refresh-repo-skill.
• scripts/check_monogs_environment.py checks Python, CUDA, native extension imports, repo modules, and optional GUI or RealSense dependencies without running SLAM.

Avoid this skill when

• The task is about generic 3D Gaussian Splatting training unrelated to MonoGS SLAM workflows.
• The user needs a CPU-only visual odometry or SLAM stack.
• The user asks to edit or maintain a different repository rather than use MonoGS workflows.