3d-cv-labeling-2026
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-enginee
npx skills add majiayu000/claude-skill-registry --skill 3d-cv-labeling-2026-curiositech-some-claude-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 3D Computer Vision Labeling Expert (2026) Expert guidance on 3D annotation tools, AI-assisted labeling workflows, and training architectures for LiDAR/point cloud computer vision in autonomous vehicles, robotics, infrastructure inspection, and geospatial applications. ## When to Use This Skill ✅ **Use for:** - Selecting 3D point cloud annotation tools (BasicAI, Supervisely, Segments.ai, Deepen AI) - Implementing SAM4D/Point-SAM for auto-labeling workflows - Designing human-in-the-loop annotation pipelines - Sensor fusion annotation (camera + LiDAR + radar) - Training architecture decisions: specialized models vs VLMs - Vertical-specific 3D detection (autonomous driving, inspection, agriculture, wildfire) ❌ **NOT for:** - 2D image labeling without 3D context (use clip-aware-embeddings or Label Studio docs) - General ML model training (use ml-engineer) - Video annotation without point clouds (use computer-vision-pipeline) - VLM prompt engineering (use prompt-engineer) - Photogrammetry/3D reconstruction (use geo processing tools) --- ## 2026 Tool Landscape Overview ### Commercial Leaders | Tool | Strength | Best For | Key AI Feature | |------|----------|----------|----------------|
- When to Use This Skill
- 2026 Tool Landscape Overview
- Commercial Leaders
- Open Source Options
- SAM Evolution for 3D (2024-2026)
- SAM4D (ICCV 2025) - Multi-Modal + Temporal
- Point-SAM (ICLR 2025) - Native 3D Prompting
- SAMNet++ (2025) - Hybrid Pipeline
- Human-in-the-Loop Architecture
- The Model-in-the-Loop Paradigm (2023-2026)
- Efficiency Gains
- Quality Assurance Strategies
- Why Specialized Training > VLMs for 3D
- The Core Trade-off
What does the 3d-cv-labeling-2026 skill do?
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-enginee
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill 3d-cv-labeling-2026-curiositech-some-claude-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.