AB3DMOT repo skill

Use this skill when a task involves AB3DMOT, 3D multi-object tracking, KITTI/nuScenes tracking inputs, the AB3DMOT tracker API, AB3DMOT result evaluation, confidence thresholding, or qualitative visualization.

AB3DMOT is a CPU-oriented Python baseline that combines 3D object detections, a 3D Kalman filter, ego-motion compensation, and Hungarian/greedy data association. It is organized as a repository script workflow rather than an installed console-entry-point package.

Start here

1. If the task is about installation, imports, dependencies, or runtime smoke checks, read references/install-and-dependencies.md and run scripts/ab3dmot_environment_check.py.
2. If the task is about repository layout, public source areas, configs, result roots, or which bundled sub-skill owns a file family, read references/repository-map.md.
3. If the task fails before you know the owning workflow, read references/troubleshooting.md.
4. For provenance and staleness checks, read references/repo-provenance.md.

Route by workflow

• Use sub-skills/data-conversion/SKILL.md for KITTI/nuScenes data placement, detector-result conversion, AB3DMOT detection row validation, category-specific detection folders, and nuScenes-to-KITTI intermediate layouts.
• Use sub-skills/tracking-pipeline/SKILL.md for main.py tracking commands, config defaults, output naming, direct AB3DMOT.track API use, Box3D/matching/Kalman behavior, and one-frame smoke checks.
• Use sub-skills/evaluation-visualization/SKILL.md for KITTI 2D/3D MOT metrics, nuScenes official/quick evaluation, confidence thresholding, result export/submission packaging, and image/video visualization.

Common task routing

User asks for	Read
“Validate this AB3DMOT detection file”	data-conversion validator and data formats
“Convert nuScenes detections for AB3DMOT”	data-conversion nuScenes conversion reference
“Run KITTI PointRCNN tracking”	tracking-pipeline tracking workflow
“Use AB3DMOT.track directly”	tracking-pipeline API reference and synthetic smoke script
“Why did main.py use nuScenes defaults?”	tracking-pipeline configuration/troubleshooting
“Evaluate KITTI validation with 0.25/0.5/0.7 3D IoU”	evaluation-visualization KITTI evaluation
“Make KITTI 2D MOT submission files”	evaluation-visualization KITTI threshold/submission guidance
“Convert AB3DMOT nuScenes results to JSON and evaluate”	evaluation-visualization nuScenes evaluation
“Render track videos”	evaluation-visualization visualization troubleshooting

Minimal runtime expectations

AB3DMOT command workflows assume a working AB3DMOT checkout or equivalent project tree with:

• Python runtime compatible with the repository and dependencies.
• NumPy and SciPy in addition to FilterPy, Numba, Matplotlib, Pillow, OpenCV, PyYAML, EasyDict, and the other requirements.txt entries.
• The external Xinshuo Python toolbox (Xinshuo_PyToolbox) importable via --toolbox-root or PYTHONPATH; it is not bundled or reliably pin-able as a PyPI dependency.
• Full external KITTI or nuScenes tracking data when running dataset-level tracking or metrics; detection text files alone are not sufficient for main.py.
• Optional nuScenes dependencies when running nuScenes conversion or official evaluation.

Safe first check from this generated skill directory, pointing it at an AB3DMOT checkout:

python scripts/ab3dmot_environment_check.py --repo-root /path/to/AB3DMOT --smoke-track

If the Xinshuo toolbox is not already importable, also pass --toolbox-root <path-to-Xinshuo_PyToolbox>.

Important constraints

• AB3DMOT is not a detector; it consumes already-generated 3D detections.
• The README quick KITTI demo command is explicit, but main.py parser defaults point at nuScenes. Always pass --dataset, --split, and --det_name deliberately.
• KITTI val maps to the external KITTI training tree and a validation sequence list. KITTI test maps to the external testing tree.
• nuScenes tracking uses the repo's KITTI-like data/nuScenes/nuKITTI/ intermediate tree.
• Local test-set labels are unavailable for KITTI and nuScenes; official test metrics require external benchmark servers.

Verification status for this generated skill

This skill's lightweight repair checks cover Python syntax and CLI help only; the synthetic tracker smoke remains dependent on an external AB3DMOT checkout, NumPy/SciPy and the Xinshuo toolbox. Full benchmark-scale tracking/evaluation requires external datasets and is intentionally not run here.