Nerd For Tech
Published in

Nerd For Tech

ByteBridge Launches 2D-3D Sensor Fusion Labeling Service, Providing Scalable Training Data for Autonomous Driving Industry

In March 2022, ByteBridge, a technology-leading data annotation service provider, announced the official launch of the 2D-3D sensor fusion labeling service. The annotation platform has been operating for a period of time. The company has cooperated with car manufacturers, autonomous driving solution developers, scientific research centers subordinate to government agencies, etc. The annotation projects include customized 3D point cloud annotation and 2D-3D sensor fusion annotation.

More read: World’s First Mobile 3D Point Cloud Data Labeling Service

ByteBridge 2D-3D Sensor Fusion Labeling Project

It is said that the key technologies of driverless vehicles mainly include environmental perception, positioning and navigation, planning and decision-making, and vehicle control. The sensors undertake the perception work. Self-driving cars can sense the surrounding environment through sensors such as onboard cameras, LiDARs, and millimeter-wave radars, monitor changes in real-time, make decisions based on the information obtained, and form a safe and reasonable path plan.

Among the sensors, the high-definition 3D point cloud, obtained by the LiDAR through the high-frequency emission of multiple laser beams can be used to detect almost all obstacles, even at night. Therefore, LiDAR has been widely recognized as an indispensable sensor for realizing unmanned driving.

A few years ago, the price of 64 multi-line LiDAR was as high as 100,000 US dollars, which discouraged many car companies. With the continuous advancement of technology, the cost of LiDAR has been greatly reduced, and the price is expected to drop to several hundred dollars in the short term. Therefore, domestic and foreign car companies have gradually applied LiDAR.

Recently, Volkswagen CEO Herbert Diess shared some thoughts on the important role of LiDAR, “Lidar technology is still expensive, but today it is the only way to offer redundant perception to the 360 camera systems which are a must. Safety is really crucial in autonomous driving. For Level 3 driving you need redundant perception.”

Different sensors have different accuracy parameters such as detection distance, resolution, directionality, and have their own advantages and disadvantages in terms of object detection, recognition and environmental adaptability. For example, LiDAR is highly susceptible to severe weather. In order to get the best detection results, the experts start to merge data from multiple sensors.

According to the Mordor intelligence report, the sensor fusion market is valued at $4.72 billion in 2020 and is expected to reach $13.62 billion by 2026, growing at a CAGR of 19.2% during 2021–2026.

Since the sensor fusion solution has become the mainstream choice for unmanned driving technology, the data annotation projects have shifted accordingly from the separate 2D images and 3D point clouds annotation to the 2D-3D sensor fusion annotation.

Regarding the 2D-3D fusion annotation project, the customization degree is relatively high. And after a period of time, the labeling requirements will change due to the improvement of the algorithm. In this case, it is necessary to have the assistance of a data labeling team with rich project experience to shape the data requirements and obtain training data that is more suitable for scenarios.

As a technology-oriented company, ByteBridge has rich R&D resources and can configure flexibly the labeling process, meet the needs of special labeling types, respond to changes timely in labeling requirements, and customize the online data delivery and review. In addition, ByteBridge provides a real-time dashboard and a 3D point cloud QC tool. Customers can manage projects in real-time and submit synchronous feedback.

According to the World Economic Forum, more than 12 million fully autonomous cars are expected to be sold per year by 2035, and autonomous vehicles will account for 25% of the global automotive market.

The scale of data is an inevitable basis of autonomous driving. The more accurate annotation is, the better algorithm performance will be. In addition, about data bias, it is necessary to find problematic scenes and supplement enough data.

ByteBridge currently cooperates with domestic and foreign traditional vehicle manufacturers and well-known algorithm companies to provide one-stop data annotation solutions, empowering the auto-driving industry.

Final Words

If you need data labeling and collection services, please have a look at

If you would like to have a look at the live demo, please feel free to contact us:




NFT is an Educational Media House. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. To know more about us, visit

Recommended from Medium

Few-shot Learning with Prototypical Networks

Understanding and debugging deep neural networks: Part I

Generalizing Your Model: An Example With EfficientNetV2 and Cats & Dogs

Image style transfer by Pytorch

Sampling for Deep Learning with BatchFlow.Sampler

End to End Machine Learning Workflow on Oracle Autonomous Data Warehouse

3D Medical Image Analysis with PyTorch

Building a Serverless Machine Learning API using ML.NET and Azure Functions

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store


A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. —

More from Medium

Hayden AI Awarded Patent for End-to-End System that Detects Traffic Violations

Vanishing Point Detection

Hand Detection in 3D Space

Am I Allergic to This? Developing a Voice Assistant for Sight Impaired People