executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
splatamRoutes agents working with SplaTAM RGB-D SLAM, Gaussian-splat reconstruction, iPhone/NeRFCapture capture, evaluation, export, and troubleshooting workflows.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this repo skill when a task involves SplaTAM: dense RGB-D SLAM with 3D Gaussians, post-SplaTAM Gaussian-splat optimization, reconstruction evaluation/export/visualization, or iPhone/NeRFCapture live capture.
Use this skill when the user asks to:
params.npz outputs to PLY splats or inspect/evaluate saved reconstructions.Avoid this skill when the task is generic 3D Gaussian Splatting unrelated to SplaTAM, pure dataset conversion with no SplaTAM config/output target, or paper-level reproduction without a SplaTAM checkout.
Work from a SplaTAM checkout root that has the expected scripts/, configs/, datasets/, utils/, and viz_scripts/ layout.
Read references/environment.md before installing or changing dependencies. SplaTAM is CUDA-first; CPU-only is not a supported substitute for the selected workflows.
If you still need to set up the runtime, follow the baseline install in references/environment.md (conda create, conda install, then pip install -r requirements.txt) before any workflow-specific changes.
Run the bundled environment check before expensive work:
python scripts/check_env.py --require-cuda --require-rasterizer
Read references/data-and-configs.md before editing Python config modules or relocating datasets.
Read references/troubleshooting.md when imports, CUDA, dataset paths, W&B, Open3D, or the custom rasterizer fail.
| User intent | Route | Read next |
|---|---|---|
| Offline RGB-D SLAM, mapping/tracking config, checkpoints, export PLY, final/online visualization, post-SplaTAM optimization, GT-pose Gaussian splatting, novel-view evaluation | sub-skills/reconstruction/ | sub-skills/reconstruction/SKILL.md |
| iPhone or LiDAR Apple device capture, NeRFCapture DDS streaming, online demo, capture-only dataset writing, capture-to-offline reconstruction | sub-skills/capture/ | sub-skills/capture/SKILL.md |
| Cross-cutting install/backend/data/config failures | Root references | references/environment.md, references/troubleshooting.md |
SourceFileLoader and expect a module-level config dictionary.config["workdir"] / config["run_name"], usually including a copied config.py, params.npz, evaluation image/metric folders, optional checkpoints, and optional splat.ply after export.diff_gaussian_rasterization; a working NVIDIA CUDA PyTorch stack is required.use_wandb=True for some benchmark presets. Disable or redirect W&B before local non-credentialed runs.sudo sysctl; do not run them without explicit authorization. Prefer the Python capture entry points when avoiding system mutation.scripts/check_env.py checks Python imports, torch/CUDA readiness, and the custom rasterizer without running a dataset.sub-skills/reconstruction/scripts/check_result_bundle.py validates a saved reconstruction directory and params.npz schema before export/eval handoff.sub-skills/capture/scripts/validate_nerfcapture_dataset.py validates a captured NeRFCapture dataset layout before using it in SplaTAM.Read references/repo-provenance.md before deciding whether this skill is current for a checkout. If the SplaTAM commit, dirty state, public configs, main scripts, dataset loaders, or dependency requirements changed materially, refresh this repo skill before relying on it.