[feat] make M3ED-SPOT provisionable

[vikuznetsov-nvidia]

The converter merged in #144 but m3ed_spot was absent from DATASETS, so nothing could build or upload its tarball. Register it with no EvalSpec, the same shape tum and icl_nuim had between their converter landing and their evaluation records.

Registering alone was not enough. Provisioning runs the registry on PYTHONPATH only, which is right for a standard-library module, but a converter is not one: convert_m3ed_spot imports numpy and Pillow at module scope and prepare() imports h5py to read the processed HDF5 products. In ubuntu:24.04 with a bare venv, which is what cuvslam-ci:local is, importing prepare fails with
ModuleNotFoundError: No module named 'numpy'.

Install the tools package before conversion rather than restating those dependencies per dataset in an image. They are already declared in tools/python_tools/pyproject.toml, so an image list would be the same duplication the dataset registry removed from the shell. Measured at 72 seconds and a 1.4 GiB venv against conversions that run for tens of minutes. The registry keeps its standard-library-only guarantee: it validates in that container with nothing installed.

M3ED therefore needs no dataset-specific image, and exposing it in the provisioning workflow is a one-line protected change.

Note for the next TartanGround run: provisioning now also installs the package inside the TartanAir image. pyproject pins numpy and scipy to the versions Dockerfile.dataset-provision already installs, so pip should not move them, but confirm with dry_run=true before relying on it.

Also stop using m3ed_spot as the unknown-dataset fixture. Registering a real corpus should not fail a test about error messages.

Summary by CodeRabbit

• New Features

  • Added support for provisioning the M3ED Spot dataset, enabling it to be prepared through the dataset workflow.
  • Dataset preparation now automatically makes required conversion tools and dependencies available in temporary storage without modifying the checkout.

• Tests

  • Updated dataset validation coverage to use a guaranteed-invalid dataset identifier, improving reliability when checking unknown datasets.

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• tools/python_tools/cuvslam_tools/dataset_registry.py
• tools/python_tools/cuvslam_tools/tests/test_dataset_registry.py

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📝 Walkthrough

Walkthrough

The provisioning script now installs local Python tools temporarily. The dataset registry now includes the provisionable m3ed_spot dataset. The registry test uses a guaranteed-invalid identifier for unknown-dataset validation.

Changes

Dataset provisioning

Layer / File(s)	Summary
Temporary Python tool installation scripts/provision_dataset.sh	The script installs tools/python_tools with pip into a temporary location and cleans up the temporary copy.
m3ed_spot registration and lookup validation tools/python_tools/cuvslam_tools/dataset_registry.py, tools/python_tools/cuvslam_tools/tests/test_dataset_registry.py	The registry adds m3ed_spot as provisionable without evaluation records. The unknown-dataset test now uses no_such_dataset.

Priority: ⬇️ Low

Estimated code review effort: 2 (Simple) | ~10 minutes

Merge Risk: ⚪ Minimal · up to 2a840

This change enables M3ED-SPOT provisioning by installing its local conversion tools and registering the dataset without evaluation records. The current changes preserve dataset lookup behavior and have no identified merge-blocking risk.

Suggested reviewers: slepichev

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 3 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely describes the primary change: making the M3ED-SPOT dataset provisionable.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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• Create PR with unit tests
• Commit unit tests in branch vikuznetsov/provision-m3ed-spot

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Test Results

Status	Platform	Language	Total	Passed	Failed	Errors	Skipped
✅	Orin	C++	17	17	0	0	0
✅	Orin	Python	74	73	0	0	1
✅	Thor	C++	17	17	0	0	0
✅	Thor	Python	74	73	0	0	1
✅	x86_64	C++	17	17	0	0	0
✅	x86_64	Python	74	73	0	0	1

cuVSLAM Evaluation KPIs

Config	Dataset	ATE, %	ARE, º/m	Kabsch	Losts	diff ATE, %	diff ARE, º/m	diff Kabsch	diff Losts	FPS, Hz
x86_64-cuda12.6.3-ubuntu24.04	EUROC-VIO_ODOM	1.6501	0.1495	0.0960	0	0.0002	0.0000	0.0001	0	119.8
x86_64-cuda12.6.3-ubuntu24.04	EUROC-VIO_SLAM	1.7785	0.1925	0.0590	0	-0.0064	-0.0003	-0.0005	0	98.4
x86_64-cuda12.6.3-ubuntu24.04	ICL_NUIM-RGBD_ODOM	2.0240	0.3931	0.0323	0	NA	NA	NA	NA	70.8
x86_64-cuda12.6.3-ubuntu24.04	ICL_NUIM-RGBD_SLAM	1.7855	0.3371	0.0270	0	NA	NA	NA	NA	70.2
x86_64-cuda12.6.3-ubuntu24.04	KITTI-STEREO_ODOM	0.8149	0.0023	2.8494	0	0.0034	0.0000	0.0440	0	246.0
x86_64-cuda12.6.3-ubuntu24.04	KITTI-STEREO_SLAM	0.7220	0.0020	1.8342	0	-0.0050	0.0000	-0.0646	0	175.3

Artifacts

• eval-html-report
• eval-kpis-pr
• test-results-x86_64
• docs-html
• test-results-orin
• test-results-thor