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Segments.ai

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About

Segments.ai is a specialized data labeling platform designed specifically for the rigorous requirements of computer vision engineers working in robotics and autonomous driving. The platform addresses the critical challenge of maintaining consistency across diverse data modalities by allowing users to label multiple sensors, such as LiDAR point clouds and 2D camera images, simultaneously. Its core functionality enables the projection of 3D labels onto 2D frames, ensuring that tracking IDs remain synchronized throughout a sequence. This multi-sensor fusion approach significantly reduces the time spent on manual data reconciliation and quality checks, resulting in higher-quality ground truth for model training. The software features advanced machine learning-assisted tools to accelerate the annotation workflow. For 2D images, the Superpixel 2.0 tool uses ML models optimized for automotive use cases to divide images into coherent regions, allowing for precise segmentation with simple clicks. For 3D data, Segments.ai supports point clouds of unlimited size and provides automated cuboid propagation across sequences. Engineers can leverage real-time interpolation and ML-powered object tracking to handle moving objects efficiently. The platform is built to be developer-friendly, offering a robust Python SDK and direct exports to popular machine learning frameworks like PyTorch, TensorFlow, and Hugging Face. This solution is best suited for startups and enterprise-level engineering teams in the automotive, robotics, and drone industries. It caters to roles ranging from individual computer vision researchers needing a free license for academic work to large-scale labeling operations requiring SOC2-level security and custom workflow integrations. By providing a white-glove service through integrated labeling partners, Segments.ai acts as both a software interface and a gateway to professional labeling workforces, offering flexibility in how data is processed and verified. What differentiates Segments.ai from general-purpose labeling tools is its deep focus on temporal consistency and sensor fusion. Instead of treating images and point clouds as isolated files, the platform treats them as integrated data streams. Features like the Batch Mode for dynamic objects and the Merged Point Cloud for stationary environments allow for context-aware labeling that is often missing in broader annotation platforms. Additionally, its technical foundations, including webhooks and active learning pipelines, ensure it integrates deeply into an organization's existing MLOps stack.

Pros & Cons

Enables one-click label projection from 3D cuboids to 2D images for significantly faster workflows.

Supports point clouds of unlimited size with advanced batch and merged modes for complex environments.

Seamlessly exports data to major frameworks like PyTorch and Hugging Face without requiring file conversions.

Provides enterprise-grade security with ISO 27001 and TISAX certifications for sensitive automotive data.

Offers a robust Python SDK that allows for deep automation and integration into existing MLOps pipelines.

The entry-level Core plan starts at $9,600 per year, which may be prohibitive for individual developers.

Point clouds on the basic Core plan are restricted to a maximum of 500,000 points per file.

Pricing for the Fusion and Enterprise tiers is not transparent and requires a custom quote from sales.

Advanced sensor fusion and webhook features are excluded from the entry-level pricing tier.

Use Cases

Autonomous vehicle engineers can synchronize tracking IDs between LiDAR and camera feeds to ensure high-quality training data.

Robotics startups can use ML-assisted segmentation to quickly build their first datasets for navigation and obstacle avoidance.

Research teams can utilize free academic licenses to process complex 3D point cloud sequences for computer vision papers.

Enterprise ML teams can automate their quality assurance process using custom metrics dashboards and dedicated support engineers.

Drone delivery companies can leverage multi-sensor fusion to label aerial data across various camera modalities and sensors simultaneously.

Platform
Web
Task
data labeling

Features

multi-sensor labeling

3d point cloud annotation

qa and organization management

cloud bucket integrations

active learning pipelines

python sdk & api

ml-powered object tracking

superpixel 2.0 segmentation

FAQs

Does Segments.ai support LiDAR and 3D point clouds?

Yes, the platform is built for 3D point clouds and supports files of unlimited size on higher tiers. It includes specialized tools like merged 3D mode, automated cuboid tracking, and batch mode for dynamic objects.

Can I export my labeled data to PyTorch or TensorFlow?

The platform offers smooth integration with popular ML frameworks including PyTorch, TensorFlow, and Hugging Face. This allows engineers to export data directly without the need for manual framework conversion.

How does the multi-sensor fusion feature work?

The platform lets you simultaneously view 2D images and 3D point clouds. You can project labels from 3D sensors to 2D sensors with one click, ensuring consistent tracking IDs across all modalities and time frames.

Is there a free version for researchers or students?

Yes, Segments.ai provides free academic licenses specifically for students and researchers. They also offer flexible options for early-stage startups depending on their specific needs and development stage.

Pricing Plans

Core
USD9600.00 / per year

3,600 hours/yr of labeling usage

Unlimited seats and projects

Image, point cloud and sequence interfaces

ML-powered labeling tools

Cloud bucket integrations

Unlimited image resolution

Point clouds up to 500,000 points

Active learning pipelines

QA solutions

Unlimited API & SDK access

Fusion
Unknown Price

5,000 hours/yr of labeling usage

All Core functionalities

Unlimited point cloud size

Sensor fusion interfaces

Webhooks system

Priority support

Metrics dashboard

Mail, Slack & Discord support

Enterprise
Unknown Price

150,000+ hours/yr of labeling usage

All Fusion features

Custom interfaces

Custom pipelines and workflows

Influence on technical roadmap

SSO and MFA

Dedicated solutions ML engineer

Tailored metric dashboards

Job Opportunities

Segments.ai favicon
Segments.ai

Sales Development Representative

Generate consistent 2D and 3D annotations for robotics and autonomous vehicles with ML-powered multi-sensor labeling tools and seamless framework integrations.

salesremotefull-time

Benefits:

  • Fully remote

  • Flexible holidays

  • Amazing retreats

  • Flexible working hours

  • Financial wellbeing

Experience Requirements:

  • 3+ years of experience in selling B2B or enterprise SaaS products

  • Experience with sales tools including CRM software, sales automation techniques and/or SEO

  • STEM background (plus)

  • Previous startup experience (plus)

Other Requirements:

  • Exceptional communication skills in English

  • Entrepreneurial attitude and eagerness to take initiative

  • Ability to prioritize and thrive in a fast-paced remote environment

Responsibilities:

  • Identify and execute on both inbound and outbound strategies

  • Own prospecting, generation and qualification of leads

  • Create insights from analytics to upgrade our sales engine

  • Talk with prospects, from computer vision engineers to CxOs

  • Investigate better solutions for prospects with product & engineering team

Show more details

Senior Product Engineer

Generate consistent 2D and 3D annotations for robotics and autonomous vehicles with ML-powered multi-sensor labeling tools and seamless framework integrations.

engineeringremoteBE
€120k - €150k
full-time

Benefits:

  • Competitive compensation

  • Stock options

  • Work from home

  • Flexible working hours

  • Flexible PTO

Experience Requirements:

  • Strong technical skills in TypeScript and modern frontend frameworks (Vue.js or React)

  • Experience with or interest in 3D graphics (Three.js/WebGL/WebGPU)

  • Senior engineering experience making others better

Other Requirements:

  • Ideally based in Belgium, or located in a timezone between GMT-1 and GMT+2

  • Comfortable working directly with customers to understand their needs

  • Write code that others want to read and maintain

Responsibilities:

  • Build visualization and annotation tools involving complex technical challenges

  • Migrating 3D rendering pipeline from WebGL to WebGPU

  • Splitting up massive point clouds into smaller tiles served dynamically

  • Implementing multi-sensor annotation features across frames

  • Shipping issue anchoring for precise problem pinpointing

Show more details

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