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evaluation【数据与研究】Guides MASt3R-SLAM benchmark dataset manifests, evaluation command planning, headless suite runs, output paths, and evo_ape metrics.
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Use this sub-skill when the task is about TUM-RGBD, 7-Scenes, EuRoC, or ETH3D
benchmark workflows: dataset acquisition planning, sequence lists, headless
suite runs, --no-calib variants, trajectory output paths, or evo_ape metrics.
evo_ape path rules.eval_*.sh command plan safely.Print the download plan:
python sub-skills/evaluation/scripts/plan_downloads.py --suite tum --commands
Print the evaluation plan:
python sub-skills/evaluation/scripts/plan_evaluation.py --suite tum --no-calib
Verify checkpoints, datasets, and available runtime budget.
Execute only the approved subset, usually one sequence first.
Run metric-only mode when trajectories already exist instead of rerunning SLAM.
eth3d.yaml evaluation config and does not expose --no-calib
in the upstream eval script.main.py in single-threaded, headless mode and
then call evo_ape tum ... -as.This sub-skill adapts the upstream shell scripts into print-first Python planners. It should not auto-download archives or run multi-hour evaluations unless the user explicitly approves that side effect.