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
Task
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
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.
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.
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
Ratings & Reviews
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