3D point cloud annotation tool

If your goal is to train a machine-learning model on 3D point clouds, my top recommendation is Supervisely for advanced 3D annotation, or CVAT if you want a free, self-hosted starting point. If you're building an autonomous-driving or robotics system with large volumes of LiDAR data, I'd also evaluate Segments.ai and commercial platforms such as Encord.

The key is to choose a tool that produces accurate, exportable training labels for your specific task—not just one with a good 3D viewer. Support for 3D bounding boxes, point-wise segmentation, temporal tracking, and your model's target data format can make a big difference.docs.supervisely.com+2

Best 3D point cloud annotation tools

• 

1. Supervisely

(Best overall for advanced workflows)

Best for teams that want to annotate data, automate labeling, manage datasets, and train models within one computer-vision platform.

• 3D cuboids, point-level segmentation, and object tracking.
• AI-assisted labeling and synchronized camera/point-cloud views.
• Handles large point clouds, with documentation describing scenes of up to 50 million points per frame.

Trade-off: More platform than you may need for a small research project.

Website ·3D documentation

docs.supervisely.comdocs.supervisely.comsupervisely.com

---

• 

2. CVAT

(Best free starting point)

Best if you want an open-source workflow, self-hosting, and control over your data and infrastructure.

• 3D annotation alongside image and video labeling.
• APIs, SDKs, and dataset import/export workflows.
• Supports connecting machine-learning models for assisted annotation.

Trade-off: Verify that its current 3D workflows cover your exact segmentation, tracking, and sensor-fusion requirements.

Website ·GitHub

github.comdocs.cvat.aidocs.cvat.ai

---

• 

3. Segments.ai

(Best dedicated 3D labeling option)

A strong choice for robotics, autonomous vehicles, and projects where annotation speed is a priority.

• Cuboids, keypoints, polygons, polylines, and 3D segmentation.
• Tracking and interpolation across sequences.
• Supports common point-cloud formats such as PCD, PLY, and KITTI-style binary data, with format-specific limitations.

Trade-off: A commercial service rather than a fully self-hosted open-source labeling stack.

Website ·Supported formats

segments.aidocs.segments.aidocs.supervisely.comdocs.segments.aisegments.aiencord.com

---

• 

4. Encord

Best for production-scale LiDAR datasets with multiple sensors, complex review processes, and enterprise data-management needs.

• LiDAR, camera, radar, and synchronized sensor data.
• AI-assisted pre-labeling and human review workflows.
• Supports formats including MCAP, ROS bag, PCD, PLY, nuScenes, and KITTI.

Trade-off: Primarily aimed at commercial and enterprise teams; evaluate pricing and deployment requirements.

LiDAR platform

encord.comencord.com

---

• 

5. SUSTechPOINTS

Best for experimenting with autonomous-driving 3D bounding-box annotations using an open-source tool.

• 3D box fitting, orientation adjustment, and object IDs.
• Camera-image context and point-cloud visualization.
• Can be installed from source or run using Docker.

Trade-off: A more specialized tool; check maintenance status and output compatibility before building a production pipeline around it.

GitHub repository

github.com

Which tool should you choose for model training?

The annotation type matters as much as the tool itself.

These are workflow-based recommendations, not a universal performance ranking. Confirm export compatibility with your training framework before committing.docs.supervisely.com+3

Three things to check before annotating

1. Export format: Ensure you can export labels in a form your training pipeline can consume, such as KITTI, nuScenes-compatible structures, or your own JSON/NumPy schema.
2. Label consistency: Define coordinate frames, class names, box orientation conventions, and handling of occluded or partially visible objects.
3. Annotation efficiency: Test a representative sample with your actual data. Measure labeling time, correction rate, and inter-annotator consistency—not just the number of features.

Let me narrow this down to the best fit

The biggest question is what kind of 3D model you're training.

1. What are you training?

3D object detection (bounding boxes)

Semantic segmentation (label every point)

Instance segmentation (separate individual objects)

3D reconstruction, mapping, or scene understanding

Object tracking across sequential frames

Something else

---

2. What type of data do you have?

LiDAR scans (KITTI, nuScenes, ROS, etc.)

RGB-D or depth-camera point clouds

Static PCD / PLY / LAS point clouds

Multiple sensors or mixed formats

---

3. What's your budget preference?

Free and open source

Commercial tool is fine

Either, if it saves time

---

4. Roughly how much data?

Hundreds of scans

Thousands to tens of thousands

Hundreds of thousands or more

[Find my best tool]

Choosing the "best" 3D point cloud or LiDAR annotation tool depends heavily on your budget , whether you need open-source vs. enterprise support, and your specific data formats (e.g., autonomous driving sensor fusion vs. medical volumetric data or geospatial mapping).

1. CVAT (Computer Vision Annotation Tool)

   • Best for: General-purpose, budget-conscious teams who want a feature-rich platform.
   • Key Features: Supports 3D cuboid annotation, multi-view visualization, and AI-assisted labeling. It handles multi-sensor configurations well and has an active community.
   • Pros: Free, self-hostable, and supports collaborative team workflows.

2. CloudCompare

   • Best for: Heavy point cloud processing, scientific analysis, and mesh manipulation.
   • Key Features: An open-source powerhouse for comparing, segmenting, and classifying 3D point clouds (via plugins like 3DMASC).
   • Pros: Incredibly powerful with massive files; excellent manual and geometric-based segmentation tools.
   • Cons: Less of a traditional "machine learning pipeline manager" and more of a technical processing utility.

3. SuperAnnotate

   • Best for: High-speed, AI-assisted labeling and end-to-end data pipeline management.
   • Key Features: Exceptional automation features for computer vision, robust 3D/LiDAR point cloud tooling, and integrated quality assurance workflows.
   • Pros: Fast interface, strong automation, great for scaling teams.

4. Deepen AI

   • Best for: Automotive-grade, high-precision safety and robotics applications.
   • Key Features: Tailored specifically for dense RGB/LiDAR point clouds, sensor fusion (camera + LiDAR synchronized views), and temporal tracking across frames.
   • Pros: Unmatched engineering-grade accuracy; compliant with strict industry standards.abaka.ai+1

5. Encord

   • Best for: Data-centric computer vision and complex multi-modal workflows.
   • Key Features: Handles 3D cuboids, point-cloud segmentation, and tracking across sequences alongside camera stream fusion. Includes advanced QA metrics and consensus scoring.
   • Pros: Exceptional quality-control tracking to spot annotator drift or inconsistency.abaka.ai

• Choose CVAT if you want a free, self-hosted tool with standard 3D bounding/cuboid features.
• Choose CloudCompare if you need deep, fine-grained geometric analysis and manual processing without a cloud SaaS overhead.
• Choose SuperAnnotate or Encord if you are running a large-scale enterprise project requiring automated AI-assisted labeling and stringent quality assurance tracking.abaka.ai
• Choose Deepen AI if your project requires automotive/robotics-grade sensor fusion (syncing cameras and multi-million point LiDAR frames).mindkosh.com+1

To help narrow down the ideal choice, let me know:

• What is your use case (autonomous driving, robotics, indoor scanning, forestry/geospatial, or medical)?
• Do you require sensor fusion (combining LiDAR with camera images)?
• Are you looking for a free/open-source tool to self-host or a managed commercial platform?

For labeling 3D point clouds from LiDAR, I’d prioritize tools that support 3D bounding-box (cuboid) annotation, point-wise semantic segmentation, object tracking across frames, and export formats compatible with your machine learning pipeline.

There isn’t one universally best tool: open-source projects are attractive for research and custom workflows, while specialized commercial platforms can save significant time through AI-assisted labeling and multi-sensor fusion. Here are my top recommendations based on documented capabilities and current tool comparisons.www.potatoannotator.com+2

Top LiDAR annotation tools

• 

1. CVAT

(Best open-source starting point)

CVAT supports 3D object annotation with cuboids and track-based annotation, including interpolation across sequences. It is a strong option for research teams that want control over their annotation infrastructure.docs.cvat.ai+1

Best for: Autonomous-driving prototypes, robotics research, and custom labeling pipelines.

Consider: Test its supported point-cloud formats and segmentation workflow against your exact requirements.

Official website ·3D annotation documentation

---

• 

2. Supervisely

(Best for AI-assisted labeling)

Provides 3D detection, segmentation, cuboid tracking, and synchronized 2D camera context. Its tooling also supports large point clouds and model-assisted pre-labeling.docs.supervisely.com+2

Best for: Larger datasets, autonomous driving, robotics, and teams that want annotation and model development in one platform.

Consider: Check subscription requirements and deployment options for your project.

Official website ·3D point-cloud documentation

---

• 

3. Segments.ai

A specialized option for 3D point-cloud labeling and multimodal annotation, especially when LiDAR data needs to be connected with camera images. It is worth evaluating for cuboid annotation, segmentation, and temporal labeling workflows.www.potatoannotator.com+2

Best for: Autonomous-driving datasets and sensor-fusion workflows.

Consider: Verify which 3D features are included in the plan you would use.

Official website

---

• 

4. Kognic

A specialized platform aimed at demanding automotive and ADAS data workflows. It is particularly worth considering when annotation quality, complex sensor setups, and production-scale operations matter more than having a lightweight editor.www.kognic.com+1

Best for: Automotive perception, advanced driver-assistance systems, and larger professional teams.

Consider: Request a demonstration and commercial quote; it may be more than a small research project needs.

Official website

---

• 

5. Xtreme1

An alternative to investigate if you want an open-source platform with 3D annotation and multimodal capabilities. It appears in current tool comparisons alongside CVAT and dedicated commercial offerings.www.potatoannotator.com+2

Best for: Teams exploring self-hosted annotation and customizable 3D workflows.

Consider: Validate format compatibility, segmentation features, and project maintenance before committing.

GitHub repository

---

• 

6. Encord

A broader AI data platform worth evaluating for annotation workflows that require data management, review, and quality controls alongside 3D labeling. Its LiDAR-focused comparison guide discusses its fit for production and enterprise workflows.www.potatoannotator.com+2

Best for: Teams building repeatable data operations with annotation review and governance.

Consider: Confirm that its current 3D feature set covers your particular labeling task before choosing it over a dedicated point-cloud editor.

Official website

Quick comparison

These are capability-based comparisons, not a verified ranking by user ratings. Feature availability can vary by product tier.www.potatoannotator.com+2

Which would I choose for your ML model?

• Research project or limited budget: Start with CVAT.
• Large dataset or AI-assisted annotation: Evaluate Supervisely.
• LiDAR plus camera synchronization: Compare Supervisely and Segments.ai.
• Automotive production pipeline: Evaluate Kognic and Encord alongside Segments.ai.

Before committing, test each finalist on a small representative sample and verify annotation export, coordinate conventions, class definitions, and tracking consistency.

I can also help you compare tools by price and deployment or find the best tool for my data format.