Segments.ai

Segments.ai is a dedicated platform that simplifies data annotation for machine learning teams handling multi-sensor information. It facilitates concurrent labeling of 2D imagery and 3D point clouds, featuring smart automation, batch operations, and synchronized 2D-3D views. Capabilities such as merged point cloud visualization, automatic object tracking, and adaptable workflows empower teams to generate superior training data faster, with less manual input, while maintaining precise tracking across different data types and time sequences. The platform's robust API and Python SDK ensure smooth incorporation into current data management systems, making it ideal for autonomous vehicles, robotics, and other applications reliant on rich sensor data.

Key Features

Multi-Sensor Labeling: Annotate both 2D images and 3D point cloud information from various sensors within one cohesive interface, promoting dataset uniformity and operational speed.

Integrated 2D-3D Annotation: Synchronize and project labels between 3D point clouds and 2D camera feeds, enhancing the speed and precision of multi-modal data annotation.

Batch Mode & Merged Point Cloud: Speed up the labeling of moving and stationary objects using batch processing and merged point cloud features, allowing for efficient annotation over sequences and clearer visualization of sparse data.

Automated Labeling & Tracking: Utilize automation features like keyframe interpolation and object tracking to extend labels across multiple frames, minimizing manual adjustments and accelerating the overall process.

Customizable Workflows & Collaboration: Enable team-based labeling, quality assurance, and tailored workflow design, including live collaboration and sophisticated task assignment methods.

API & SDK Integration: Connect effortlessly with existing data pipelines using the Python SDK and API for managing datasets, uploading samples, and retrieving labels.

Use Cases

Autonomous Vehicle Training Data: Create high-fidelity datasets for self-driving models by efficiently labeling combined data from lidar, radar, and cameras.

Robotics Perception Systems: Annotate intricate 2D and 3D sensor data for robotics uses, such as navigation, object manipulation, and environmental comprehension.

Quality Control for Machine Learning: Guarantee labeling accuracy and consistency across extensive datasets to reduce errors and enhance model training results.

Semantic Segmentation Projects: Generate detailed segmentation and object tracking labels for computer vision applications that demand exact object boundary definition.

Custom Data Annotation Workflows: Build specialized labeling pipelines for unique requirements, capitalizing on the platform's automation and workflow customization options.