THE CHALLENGE
In AI-driven technologies—such as autonomous vehicles, robotics, and surveillance systems—the costly and time-consuming process of labeling visual data used to train machine learning models has always been a problem. Traditional manual annotation methods require significant human effort, leading to high operational costs and slower development cycles. While newer semi-automated techniques like LiDAR-textured scanning, synthetic data projection, and SLAM (Simultaneous Localization and Mapping) offer some relief, they come with technical pitfalls—such as sensor misalignment, overfitting to unrealistic features, and difficulties in merging 3D and 2D data accurately. These challenges make it hard to scale high-quality dataset generation while maintaining accuracy and real-world relevance, ultimately limiting a company’s ability to quickly bring reliable AI products to market. As the demand for smarter, data-driven systems grows, solving this bottleneck has become a technical necessity.
OUR SOLUTION
We offer a breakthrough in generating high-quality training data for AI by drastically reducing the need for manual labeling. By combining data from LiDAR scanners and video cameras, the system creates a virtual 3D model of real-world scenes where objects are automatically identified and annotated using techniques like SLAM mapping and synthetic data projection. This automated pipeline transfers labels accurately from the virtual environment to real video footage, streamlining the entire data annotation process. The result is a scalable, cost-effective solution that accelerates AI development, reduces labor costs, and improves the accuracy of machine learning models for applications like object detection and classification—making it a smart investment for companies building next-generation autonomous or vision-based technologies.
Figure: The labeling process of the LiDAR-only approach.
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