Best Tools for Labeling 3D Files in 2026

The "best" tool for labeling 3D files heavily depends on several factors:

Type of 3D Data: Are you labeling point clouds, meshes, volumetric data (like CT/MRI scans), or CAD models?

Type of Labeling: Do you need bounding boxes (cuboids), semantic segmentation (pixel-level classification), instance segmentation (distinguishing individual objects), keypoint annotation, material annotation, or just simple text annotations for documentation?

Purpose: Is this for machine learning/AI training, quality control, documentation, or design review?

Scale of Project: Is it a one-off task, or do you need to label massive datasets with a team?

Budget & Resources: Are you looking for free/open-source solutions, or do you have a budget for commercial tools or managed services?

Required Output Format: What format does your downstream application or ML model expect (e.g., KITTI, COCO, custom JSON)?

Given these variables, here's a breakdown of excellent tools categorized by their common use cases:

I. For AI / Machine Learning Training Data

This category focuses on creating structured annotations (bounding boxes, segmentation masks, keypoints) that an AI model can learn from.

A. Point Cloud & 3D Annotation Platforms (LiDAR, 3D Scans, Multi-Modal Data)

Supervisely

Pros: Comprehensive platform for image, video, and 3D data. Supports point clouds with cuboid bounding boxes, polygonal segmentation on slices, and semantic labeling. Strong collaboration features, powerful automation (NN-based pre-labeling). Can be self-hosted or cloud-based.

Cons: Can have a learning curve; commercial for larger teams/datasets (though free tiers exist).

Best for: Large-scale AI projects with diverse data types.

Encord

Pros: Enterprise-grade data annotation platform with strong support for 3D point clouds, cuboids, semantic segmentation, and multi-sensor fusion (LiDAR + camera). Excellent workflow orchestration, quality assurance, active learning, and model-in-the-loop labeling. Designed to scale with ML teams and integrates well into modern ML pipelines.

Cons: Commercial/enterprise pricing; not intended for casual or one-off annotation tasks.

Best for: ML teams building production-grade perception systems (autonomous driving, robotics, mapping, AR/VR) that require scalable, high-quality 3D annotations and dataset management.

Scale AI / Labelbox / V7 Data

Pros: Managed data labeling services combined with robust platforms. They handle workforce, QA, and tooling. Support various 3D annotation types (cuboids, point cloud segmentation, 3D tracking).

Cons: Enterprise-grade pricing; less direct control if using managed labeling services.

Best for: Companies with large budgets and high-volume, high-quality data labeling needs (automotive, robotics, AR/VR).

CVAT (with 3D support)

Pros: Open-source, self-hostable, and customizable. Supports 3D bounding boxes for point clouds via plugins. Full control over infrastructure.

Cons: Requires setup and maintenance; 3D features are newer and less polished than commercial platforms.

Best for: Researchers, startups, or technically strong teams with limited budgets.

CloudCompare

Pros: Free, open-source, powerful for point cloud and mesh visualization, manual segmentation, and classification.

Cons: Not a dedicated ML labeling platform; limited collaboration and automation.

Best for: Academic research, small-scale manual segmentation, or pre-processing workflows.

Blender (with custom scripts/add-ons)

Pros: Free, open-source, extremely flexible. Can be scripted to create custom annotation pipelines; useful for detailed mesh-level labeling and synthetic data generation.

Cons: High learning curve; not optimized for high-throughput labeling without heavy customization.

Best for: Highly customized annotation needs or complex mesh segmentation.

B. Mesh / Object-Based 3D Model Labeling

Blender

Excellent for part-level mesh segmentation, material assignment, and object-level labeling.

3D CAD / DCC Software

Examples: Maya, 3ds Max, SolidWorks, AutoCAD

Pros: Precise part naming, assemblies, dimensions, and material metadata.

Cons: Expensive; not designed for ML-style dataset annotation.

Best for: Engineered products, architecture, or synthetic dataset creation.

II. For 3D Model Annotation & Documentation

3D PDF / Viewer-Based Annotation Tools

Modern CAD viewers and web-based platforms allow text notes, measurements, and callouts.

Dedicated 3D Viewers & Review Software

Examples: eDrawings, Autodesk Forge Viewer / A360, AR/VR inspection tools

Best for: Design review, quality control, collaboration, and documentation—not ML training.

III. For Medical & Volumetric Data (CT, MRI)

3D Slicer

Industry-standard for medical image segmentation and 3D visualization.

ITK-SNAP

Strong for manual and semi-automatic anatomical segmentation.

Key Factors to Consider When Choosing

• Data format (.las, .pcd, .obj, .stl, .nii, .dicom, .step)
• Annotation granularity (object vs per-point/per-voxel)
• Automation & AI-assisted labeling
• Scalability and collaboration
• Integration with ML pipelines and export formats

Conclusion

For AI / Machine Learning applications, especially at scale, Supervisely, Encord, Scale AI, Labelbox, and V7 Data are leading solutions.

Encord stands out for enterprise-scale 3D perception workflows, multi-sensor data, and model-in-the-loop dataset iteration.

CVAT is the strongest open-source option.

CloudCompare and Blender excel for specialized or manual workflows.

For human-readable documentation and design review, traditional CAD tools and 3D viewers remain the best fit.

As always, clearly define your data type, annotation needs, scale, and end goal before choosing a tool.