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3D annotation for computer vision v1.0.0
Use this resource to choose tools and design a 3D point cloud annotation workflow
New
- 529 downloads
- 136 likes
- 108 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install point-cloud-labeling-3dWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent compares 3D annotation tools, explains assisted segmentation models such as SAM4D or Point-SAM, and proposes an architecture with human review. You get tool recommendations and a workflow plan.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: 2026 tool overview and annotation method
- references/: SAM4D architecture and tool comparison matrix
Good fit for
- You must label LiDAR data for a driving or robotics project
- You are choosing between several 3D annotation tools
Not for
Tags
- 3D labeling
- LiDAR
- point cloud
- segmentation
About
You say
“Which tool should I use to label pedestrians in LiDAR clouds, with automatic pre-labelling?”
Skill published by curiositech under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to choose tools and design a 3D point cloud annotation workflow
- Review and adjust the result
Content preview
Files
- CHANGELOG.md0.8 KB
- LICENSE1.1 KB
- NOTICE-PALATEKIT.md0.5 KB
- README.md2.2 KB
- references/sam4d-architecture.md2.6 KB
- references/tool-comparison-matrix.md3.4 KB
- SKILL.md13.8 KB
Origin and licence
Listed unmodified
- Author
- curiositech
- Source
- github.com/curiositech/some_claude_skills/tree/f49825290077e5fb46bb3e867dfa8a5b0161152b/inbox/3d-cv-labeling-2026
- Licence
- MIT
Notice
« 3d-cv-labeling-2026 » (curiositech, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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