Segments.ai

What is Segments.ai?

Segments.ai is a specialized data labeling platform designed to streamline annotation workflows for machine learning teams working with multi-modal sensor data. The platform supports simultaneous labeling of 2D images and 3D point clouds, offering advanced automation, batch processing, and integrated 2D-3D interfaces. With features like merged point cloud mode, automated tracking, and customizable workflows, Segments.ai accelerates the creation of high-quality training datasets, reduces manual effort, and ensures consistent object tracking across modalities and time. Its robust API and Python SDK enable seamless integration into existing data pipelines, making it a preferred choice for robotics, autonomous vehicles, and other sensor-rich industries.

Key Features

• Multi-Sensor Labeling

  Label 2D images and 3D point cloud data from multiple sensors within a single unified interface, ensuring consistency and efficiency across datasets.

• Integrated 2D-3D Annotation

  Project and synchronize annotations between 3D point clouds and 2D camera images, allowing for faster and more accurate multi-modal labeling.

• Batch Mode & Merged Point Cloud

  Accelerate annotation of dynamic and static objects with batch processing and merged point cloud modes, enabling efficient labeling across sequences and improved visibility for sparse data.

• Automated Labeling & Tracking

  Leverage automation tools such as keyframe interpolation and object tracking to propagate labels across frames, reducing manual corrections and speeding up the workflow.

• Customizable Workflows & Collaboration

  Support for collaborative labeling, quality control, and workflow customization, including real-time collaboration and advanced assignment techniques.

• API & SDK Integration

  Integrate seamlessly with existing pipelines using the Python SDK and API for dataset management, sample uploads, and label downloads.

Use Cases

• Autonomous Vehicle Training Data : Efficiently label multi-modal sensor data from lidar, radar, and cameras to create high-quality datasets for autonomous driving models.
• Robotics Perception Systems : Annotate complex 2D and 3D sensor data for robotics applications, including navigation, manipulation, and environment understanding.
• Quality Control for Machine Learning : Ensure consistent and accurate labeling across large datasets, minimizing errors and optimizing model training outcomes.
• Semantic Segmentation Projects : Produce detailed segmentation and object tracking labels for use in computer vision tasks requiring precise object boundaries.
• Custom Data Annotation Workflows : Develop tailored labeling pipelines for specialized use cases, leveraging the platform’s automation and workflow customization features.

FAQs