Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 15:53 UTC

isl-org / Open3D

Open3D: A Modern Library for 3D Data Processing

C++Custom license★ 13,970 stars⑂ 2,623 forkssince Dec 2016View on GitHub ↗
KindDesktop applicationLibraryCommand-line toolhow this is determined

isl-org/Open3D holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Vitality (83/100) and lowest on Security (44/100). It was last updated today. 3 contributors account for most of its recent work.

86
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

86
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

2,011 followers69 public repossince Nov 2016

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
npmopen3dpoints to another repo — not scored0.2.47,163121264 days agogeometry3dvector

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

83Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
20.8/36Commit cadence — 30/52 weeks with commits
16.9/18Commit volume — 75 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year75
human_commit_share0.95
days_since_last_push0
active_weeks_last_year30
How it's scored
27/27Ships releases — 24 releases published
36/36Release recency — latest release 0 days ago
12.6/27Release cadence — a release every ~232.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count24
latest_release_tagv0.20.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases232.5

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

76Good · 17% of overall
How it's scored
60/60Stars — 13,970 stars
25/25Forks — 2,623 forks
12.8/15Watchers — 202 watchers
Inputs used
forks2,623
stars13,970
watchers202
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

74Good · 23% of overall
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
18/22.5Commit distribution — top contributor authored 20% of commits
13.5/13.5Contributor breadth — 98 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor3
contributors_sampled98
top_contributor_share0.199
How it's scored
29.1/42Issue resolution — 69% of issues closed
25.4/30PR acceptance — 2,429/2,865 decided PRs merged
2.6/13Newcomer PR acceptance — 1/5 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs2,429
open_issues1,266
closed_issues2,852
prs_merged_7d3
prs_decided_7d3
prs_merged_30d10
prs_decided_30d15
issue_closed_ratio0.693
closed_unmerged_prs436
first_time_authors_30d5
first_time_prs_merged_30d1
first_time_prs_decided_30d5
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
23.8/25Owner reach — 2,011 followers of isl-org
25/25Track record — 69 public repos, account ~9 yr old
Inputs used
followers2,011
owner_typeOrganization
is_verifiedno
owner_loginisl-org
public_repos69
account_age_days3,591

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall
How it's scored
24/24CI workflows — 11 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — http://www.open3d.org
10/10Repository description
10/10Topics — 20 topics
10/10Wiki
Inputs used
topicsmesh-processing, computer-graphics, opengl, cpp, python, reconstruction, odometry, visualization, registration, machine-learning, 3d, pointcloud, rendering, gui, 3d-perception, gpu, arm, cuda, pytorch, tensorflow
has_wikiyes
homepagehttp://www.open3d.org
docs_sitehttp://www.open3d.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

44Weak · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsdocs/requirements.txt, python/pyproject.toml, python/requirements.txt, python/requirements_build.txt, python/requirements_jupyter_build.txt, python/requirements_jupyter_install.txt, python/requirements_style.txt, python/requirements_sycl.txt, python/requirements_test.txt, python/requirements_win_cuda.txt, python/setup.cfg, python/setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages26
unassessed_packages26
affected_by_severityhigh 1
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 26 resolved dependencies against OSV. 26 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

83Excellent · 4% of overall
How it's scored
45/45Agent instructions — .github/copilot-instructions.md, AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 90 of 95 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.947
agent_instruction_files.github/copilot-instructions.md, AGENTS.md
agent_instruction_max_bytes6,407
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
11/11Static type checking — C++ (statically typed)
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 19 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 5 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageyes
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0.19
toolchain_manifests—
dependency_bot_commit_share0.05
How it's scored
45/45Type-checkable code — C++ (statically typed)
54/55Manageable file sizes — 31/1,738 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes1,401,900
source_files_sampled1,738
oversized_source_files31
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — python/open3d/visualization/tensorboard_plugin/plugin_data.proto
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_filespython/open3d/visualization/tensorboard_plugin/plugin_data.proto
interfaces_expected_of—
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

13,970GitHub stars
98contributors
75commits, last 12 months
0days since last push
24releases
3bus factor
1,266open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • npm package 'open3d' points at a different repository (https://github.com/ccc159/open3d); excluded from ecosystem scoring
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/17 15:52:12 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 2,623 ⇿
0Stars
2,623Forks
5Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

1,5001,7502,0002,2502,5002,7502,62392022-082024-092026-09
Major 0Minor 4Patch 0

Each point covers 4 days.

Direct dependencies 3
RegistryPackageVersion constraintManifest
npm@jupyter-widgets/base^2 || ^3 || ^4 || ^5 || ^6python/js/package.json
npmlodash^4.17.4python/js/package.json
npmwebrtc-adapter^4.2.2python/js/package.json
All dependencies 52

Full resolved dependency set from the GitHub dependency graph: 3 direct and 49 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
npm@jupyter-widgets/base^2 || ^3 || ^4 || ^5 || ^6direct
npmlodash^4.17.4direct
npmwebrtc-adapter^4.2.2direct
npm@jupyterlab/builder^4.0.0indirect
npmrimraf^4.0.0indirect
npmwebpack^5indirect
PyPIbreathe4.5.0indirect
PyPIcertifi—indirect
PyPIclang-format—indirect
PyPIconfigargparse—indirect
PyPIdash—indirect
PyPIdocutils0.20.1indirect
PyPIdpcpp-cpp-rt2026.0.0indirect
PyPIflask—indirect
PyPIfuro2023.9.10indirect
PyPIipywidgets—indirect
PyPIjinja2—indirect
PyPIjupyter-packaging—indirect
PyPIjupyterlab—indirect
PyPIlxml6.1.0indirect
PyPIlxml-html-clean0.4.4indirect
PyPImatplotlib—indirect
PyPImatplotlib3.11.0indirect
PyPImyst-parser2.0.0indirect
PyPInbconvert7.17.1indirect
PyPInbformat—indirect
PyPInbsphinx0.9.3indirect
PyPInumpy—indirect
PyPInvidia-cublas-cu1212.6.4.1indirect
PyPInvidia-cuda-runtime-cu1212.6.77indirect
PyPInvidia-cusolver-cu1211.6.4.69indirect
PyPInvidia-cusparse-cu1212.5.4.2indirect
PyPInvidia-npp-cu1212.4.1.87indirect
PyPIoauthlib3.2.2indirect
PyPIonemkl-sycl-blas2026.0.0indirect
PyPIonemkl-sycl-lapack2026.0.0indirect
PyPIopencv-python—indirect
PyPIpybind11-stubgen—indirect
PyPIpygments—indirect
PyPIpytest9.0.3indirect
PyPIpytest-randomly3.8.0indirect
PyPIscipy1.17.0indirect
PyPIsetuptools—indirect
PyPIsphinx7.1.2indirect
PyPIsphinx-copybutton0.5.2indirect
PyPIsphinx-tabs3.4.7indirect
PyPItensorboard2.19.0indirect
PyPItinycss2—indirect
PyPIwerkzeug—indirect
PyPIwheel—indirect
PyPIwheel0.46.3indirect
PyPIyapf—indirect
Dependency advisories 1

This repository publishes no package the index resolves, so its own dependency graph was assessed — 26 packages, which also include development and test pins that never ship: 1 carry known advisories, of which 0 are direct. 26 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
lxml-html-clean0.4.4indirecthigh20.4.5

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — npm.