Agent skill · research science · erichowens
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-engineer).
Why this skill is useful
Adds detailed workflows and domain-specific knowledge for 3D computer vision annotation that the AI wouldn't reliably generate on its own.
What it needs
About 6k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
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 ------ ---------- ---------- ---------------- BasicAI One-click detection Autonomous driving Pre-labeling models fine-tuned for AV Supervisely Customization R&D teams AI tracking, 2D→3D single-click Segments.ai 2D+3D sync Robotics perception Sequential propagation Deepen AI Sensor calibration In-house perception Pixel-perfect multi-sensor Dataloop Enterprise MLOps Large annotation teams Model-assisted + Point Cloud Focus Encord Full workflow Multi-modal projects Track-ID management Ango Hub (iMerit) Dense annotation Complex multi-modal Frame-to-frame propagation Open Source Options Tool Maturity Limitations ------ ---------- ------------- CVAT Stable 3D bounding boxes only, limited interpolation 3D BAT Good Full-surround annotation, semi-auto tracking Label Studio Partial 3D Better for multi-format, not specialized 3D --- SAM Evolution for 3D (2024-2026) SAM4D (ICCV 2025) - Multi-Modal + …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/3d-cv-labeling-2026