Segments.ai provides a data labeling platform designed for computer vision engineers working with robotics and autonomous vehicle data. The platform specializes in simultaneous multi-sensor annotation, enabling consistent and accurate labeling across 2D images and 3D point clouds. Key features include efficient 3D cuboid projection, ML-powered tracking, and advanced image segmentation tools to accelerate ground truth generation.
Funding
$1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
VVFounders
Product
Problem
Machine learning teams in robotics and autonomous vehicles face challenges in efficiently labeling multi-sensor data, including images and 3D point clouds. The process is often time-consuming and requires significant effort for quality checks and corrections, hindering the development and deployment of advanced perception models.
Solution
Segments.ai offers a comprehensive platform designed to streamline multi-sensor data labeling, enabling machine learning teams to annotate images and 3D point clouds simultaneously. The platform leverages deep learning to facilitate instance and semantic segmentation, reducing the time spent on manual annotation and quality control. By providing a unified interface for labeling across various data modalities, Segments.ai ensures consistency and accuracy, accelerating the development of robust perception models. Advanced automation features, such as projection and copying of labels between 3D and 2D sensors, further enhance labeling speed and consistency.
Target Audience
Segments.ai primarily targets machine learning teams and computer vision engineers in the robotics and autonomous vehicle industries who require efficient and accurate multi-sensor data labeling solutions.
Features
- Unified interface for simultaneous labeling of images and 3D point clouds
- Deep learning-powered instance and semantic segmentation
- Advanced automation for projecting and copying labels between 3D and 2D sensors
- Batch mode for efficient labeling of dynamic objects across sequences
- Merged Point Cloud mode for accurate annotation of stationary objects
- Superpixel tool for rapid image segmentation with adjustable granularity
- Autosegment tool for segmenting small objects and high-resolution images
- Integration with Segment Anything Model for one-click segmentation of generic objects
- Python SDK and API for seamless integration into existing machine learning workflows
- Support for uploading point clouds of unlimited size