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name: gaussian-splatting description: "Routes official 3D Gaussian Splatting setup, data preparation, training, rendering, evaluation, and viewer workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Gaussian Splatting Repo Skill

Use this repo skill when the user needs to operate or troubleshoot the official GraphDeco/Inria gaussian-splatting implementation for 3D Gaussian Splatting: CUDA setup, COLMAP/Blender scene preparation, optimizer training, offline rendering/metrics, paper-style evaluation, or SIBR viewers.

Start Here

• Read references/repo-provenance.md before deciding whether this skill is current for a checkout.
• Read references/setup-and-backends.md for a cross-cutting summary of Python/CUDA/submodule requirements.
• Read references/troubleshooting.md when you need to route an error to the nearest troubleshooting page.
• Use scripts/check_3dgs_environment.py for a safe broad preflight. It does not run training, rendering, COLMAP, or SIBR.

Route Map

User task	Read
Install the repo, initialize submodules, build CUDA extensions, check PyTorch/CUDA, debug imports	sub-skills/setup-and-backends/SKILL.md
Prepare images, validate COLMAP/Blender scene layouts, run conversion, create depth regularization files	sub-skills/data-preparation/SKILL.md
Build or debug train.py commands, choose feature flags, resume checkpoints, handle OOM/debug/viewer training issues	sub-skills/training/SKILL.md
Run or diagnose render.py, metrics.py, pretrained model evaluation, full_eval.py, output/result JSON layouts	sub-skills/rendering-evaluation/SKILL.md
Build/run SIBR remote or real-time viewers, connect to optimizer, use top view, debug OpenGL/CUDA interop	sub-skills/viewers/SKILL.md

Minimal Preconditions

The core Python workflows require:

• a CUDA-capable NVIDIA GPU for real train/render/metrics execution,
• PyTorch with CUDA support,
• built/importable diff_gaussian_rasterization and simple_knn extensions,
• a prepared COLMAP or Blender/NeRF synthetic scene for training,
• a trained model directory for rendering/evaluation.

A CPU-only environment can run validators and command builders, but it does not verify the core optimizer or renderer.

Safe Preflight

For an editable checkout or source tree, run:

python scripts/check_3dgs_environment.py --repo-root <checkout> --require-cuda --require-extensions --check-tools

If this fails on CUDA or extensions, route to setup/backends before training or rendering. Missing colmap, magick, or SIBR binaries matters only for conversion or viewer tasks.

Common Workflow Chain

1. Setup/backend: prove CUDA and extensions.
2. Data preparation: validate <scene> and optional depth files.
3. Training: run train.py -s <scene> -m <model> --eval --disable_viewer or a tailored variant.
4. Rendering/evaluation: run render.py -m <model> and metrics.py -m <model>.
5. Viewer: run a SIBR real-time viewer with -m <model> or connect a remote viewer to an optimizer.

Boundaries and Warnings

• Do not run full training, full evaluation, dataset downloads, pretrained model downloads, COLMAP conversion, or SIBR builds as implicit smoke checks; ask for explicit compute/time/network approval.
• Do not tell users that CPU validation proves CUDA training/rendering.
• Do not rely on original repository docs or scripts at runtime when a bundled reference/script covers the needed operation.
• Respect the repository's non-commercial research/evaluation license terms.