Segments.ai

Segments.ai is a multi-sensor data labeling platform for robotics and autonomous vehicle teams. It supports image and point-cloud annotation using segmentation, cuboids, bounding boxes, keypoints, polygons and polylines. Teams can view 2D images beside 3D point clouds and transfer labels between sensors. For sequences, Batch Mode, merged point clouds, interpolation and automated tracking support work across objects and frames. Image tools include Superpixel, Autosegment, hover-and-click segmentation, and uploaded model predictions that annotators can review and correct. A Python SDK supports data uploads, label downloads, dataset management and webhooks; TensorFlow and PyTorch are named integrations. Dataset exports include JSON, PyTorch, TensorFlow and Hugging Face formats. The company says the product launched in 2020 as a segmentation-oriented labeling solution and is designed for computer vision engineers and data scientists in autonomous robotics and driving. A free academic license is available for non-commercial research, subject to a license request. Core, Fusion and Enterprise use custom quotes; Core supports point clouds up to 500,000 points each, while Fusion includes point clouds and images of unlimited size and resolution. The company states that it is EU based, ISO 27001 certified and GDPR compliant.

Segments.ai offers annotation tools for images, point clouds and multi-sensor sequences, with model-assisted options and dataset integrations. Its paid plans require custom quotes, and Core has a stated point-cloud size limit.

Segments.ai is aimed at computer vision engineers and data scientists working in autonomous robotics and driving. Its multi-sensor tools also suit teams labeling image and point-cloud datasets together.

Segments.ai is a multi-sensor labeling platform for robotics and autonomous-vehicle teams. It best suits computer-vision engineers and data scientists handling images, point clouds, and sequential sensor data. Its strongest case is coordinated 2D/3D annotation with automation, while quote-based pricing and Core’s point-cloud ceiling narrow its appeal for small or occasional projects.

Overview

Segments.ai combines image and point-cloud labeling in one workflow. Users can view both sensor types together and project labels between sensors, an important advantage when a dataset must remain consistent across cameras and 3D sensors. The product launched in 2020 as a segmentation-focused solution and is delivered through web and API access.

Teams can collaborate, manage datasets programmatically, and export annotations in JSON, PyTorch, TensorFlow, and Hugging Face formats. Security-conscious organizations get an EU-based service that states ISO 27001 certification and GDPR compliance.

Key features

Broad 2D and 3D annotation

Supported types include segmentation, bounding boxes, cuboids, polygons, polylines, and keypoints. Images can be JPEG, PNG, or BMP. That range suits projects mixing object detection, instance or semantic segmentation, and pose-style keypoint work; teams needing only one simple label type may not benefit as much from the broader toolset.

Multi-sensor sequence work

Image and point-cloud views can be synchronized, with labels projected between sensors. Batch Mode, merged point clouds, interpolation, and automated tracking address objects that persist across 3D sequences. These tools target robotics and driving datasets where frame-by-frame redrawing would otherwise consume substantial labeling time.

Model-assisted labeling

Image workflows include Superpixel, Autosegment, hover-and-click segmentation, and the ability to upload model predictions for review and correction. This is useful when a team already has models that can propose labels, while human correction remains part of the process rather than being replaced.

API and integrations

The Python SDK handles programmatic uploads, label downloads, dataset management, and webhooks. The integrations page names TensorFlow and PyTorch, and the export options also include Hugging Face. Core adds cloud-bucket integrations, making it better suited to teams that keep source data in cloud storage.

Pricing

Segments.ai offers a 14-day trial and a free option, but paid plans use custom quotes. The usage commitments and limits matter more than a simple seat price.

Academic license

The Academic license costs nothing for non-commercial academic research, billed 100% free. It is intended for students and researchers and requires a license request. Commercial teams cannot use this as a general-purpose free tier.

Core

Core is custom quote pricing and starts at 3,600 hours per year of labeling usage, equivalent to $2.67 per hour. It includes unlimited seats, projects, images, and point clouds, plus cloud-bucket integrations. Each point cloud can contain up to 500,000 points. That cap may be workable for many datasets but rules out larger scans unless they are reduced or handled elsewhere.

Enterprise

Enterprise uses custom pricing and includes more than 150,000 hours per year of labeling usage, with unlimited seats, projects, images, and point clouds. It is the plan for organizations with very large annual workloads; the published plan summary does not state a point-cloud size limit.

Fusion

Fusion is custom quote pricing and starts at 5,000 hours per year. It includes unlimited seats, projects, images, and point clouds, with unlimited point-cloud and image size and resolution. Priority support plus mail, Slack, and Discord support make it the clearest fit for teams that need a higher-touch service model.

Platforms

Segments.ai runs on the web and provides an API, including Python-based programmatic access. It is therefore suited to teams that want browser annotation connected to data pipelines rather than a standalone desktop workflow.

Who it's for

Choose it for autonomous-vehicle, robotics, and other computer-vision teams whose datasets combine 2D imagery, 3D point clouds, and sequences. The multi-sensor projection, sequence tools, model-assisted labeling, and SDK are meaningful advantages for that audience. Smaller teams with occasional labeling needs, image-only projects, or point clouds larger than 500,000 points should examine the commitment and Core limit carefully.

Pros and cons

• Pro: One workflow covers images, point clouds, and cross-sensor label projection.
• Pro: Sequence features such as interpolation and automated tracking reduce repetitive labeling across frames.
• Pro: Model predictions, Autosegment, and other assisted tools support correction-based workflows.
• Pro: API, webhooks, cloud-bucket integrations, and machine-learning exports fit production pipelines.
• Con: Paid plans require custom quotes and substantial annual usage commitments.
• Con: Core limits each point cloud to 500,000 points, while only Fusion explicitly supports unlimited size and resolution.
• Con: The academic free license is restricted to non-commercial research and requires approval.

Alternatives

Roboflow is the better starting point when a freemium model, mobile platforms, or a stated free allowance of 10 credits per month matters more than Segments.ai’s multi-sensor focus.

CVAT suits personal users and small teams seeking a free MIT-licensed community edition or self-hosted deployment.

Labellerr is worth considering for researchers who want a free plan with 2,500 data credits, one seat, and one workspace.

Annota AI is a simpler web or self-hosted option with a free tier capped at one member, two datasets, 4 GB of storage, and €4 in monthly credits.

Draw Attention targets interactive-image annotation rather than a broad robotics labeling workflow.

Label Studio is an alternative with a free Community Edition and broad platform coverage, including desktop operating systems.

Supervisely offers a free community plan with 5 GB of storage, 10,000 files, and two members.

Ultralytics Platform is a better fit when built-in cloud training and deployment quotas are the priority.

For broader comparisons, see Image Annotation Software, AI Image Annotation Tools, and Data Labeling Software.

Verdict

Segments.ai is a strong choice for professional robotics and autonomous-driving teams that need coordinated image, point-cloud, and sequence annotation backed by APIs and model-assisted tools. Choose it when multi-sensor consistency and production-scale workflows justify custom pricing. Look elsewhere if your work is small, image-only, highly price-sensitive, or depends on point clouds above Core’s 500,000-point limit without a Fusion budget.