Significant advancements in sensor hardware, software, transmission, and data storage technologies have paved the way for a bright future of multi sensor fusion field through 2D-3D linking and labeling and camera, radar, and LiDAR output annotation with greater accuracy, speed, and effectiveness. Multidisciplinary team of a video, data, and audio labeler has become so crucial in cutting-edge applications based on artificial intelligence and machine learning. According to the Allied Market Research forecast, the market value of sensor fusion is expected to reach $19.84 billion by 2030 from just $3.55 billion in 2020 with a gigantic growth of over 19.7% CAGR during the projection period. This is a clear indication of the optimistic future of 2D 3D sensor fusion object detection classification identification services in the world. Thus, we can say that sensor fusion shapes the future of connected devices completely.

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What Are the Different Types of Sensors Used in Sensor Fusion?

The most common types of sensors used in the computer-vision and environment detection-based real-world applications such as driverless cars, internet of things, wearables, navigation and geo data services include:


Light Detection and Ranging, precisely referred to as LiDAR, is a sensor device that is used for measuring the distance between the target object and the sensing device. It uses infrared waves for ranging the items within the infrared range by recording the time infrared waves take to return back after bouncing off from the target body. With the help of LiDAR annotation specialists, this data can be easily used to plot a 3D point cloud dataset to be fused into other input data by different algorithms and models.


Camera is a very popular and cost-efficient sensing device whose output in the form of an image and video can easily be used for building 3D model computer vision datasets and automated interpolation frames for multi sensor fusion in modern computer vision applications powered by artificial intelligence and machine learning technologies. Specialized 3D point cloud camera is a very useful example extensively used in contemporary sensor data services.


A radar is a traditional sensor that is extensively used in modern computer vision technologies too. It is used for measuring the speed through continuous and auto ML object detection method referred to as radial frequency measuring function. A radar uses radio waves for the accurate measurement of speeds and directions of the movements of objects. The output can be fused into other sensor data to get an accurate output of an environment.

Annotated Map

An annotated map can be a 3D point cloud or an image dataset prepared by data annotators with detailed 3D annotation and labeling. It is used as a sensor input to the projects based on machine learning, especially to computer-vision (CV) applications for training purposes. An annotated map may include numerous types of annotation such as 2D, 3D boxes, semantic segmentation, polygons, and lines and splines. 

What Are the Main 3D Sensor Fusion Data Labeling Services?

To achieve a perfect sensor data fusion output, you need to deal with numerous labeling and annotation services such as object detection, tracking, linking, and object classification professionally.

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A few of the most important services extensively used for sensor fusion functions include:

3D Object Tracking

In a 3D object tracking service, the detailed object identification is assigned to the objects in different frames made of images, 3D clouds points, or videos. The main objective of this service is to detect and track the object very accurately in multiple frames. The first step of this service is detection of an object in 2D crop, the second step takes the entire image crop to estimate 3D boxes. At the same time, the 2D crop for the object is computed for the next frame too. 

2D & 3D Linking

The detection and linking of 2D and 3D objects in multiple frames with exclusive identities and additional attributes is known as 2D-3D linking. In certain cases, 2D objects are annotated with additional attributes manually to make them 3D objects is referred to as 2D to 3D linking service. The linking is done across multiple frames for smooth understanding and prediction.

Bird’s-Eye-View Labeling

Bird’s-Eye-View labeling is a service to build datasets out of the 3D cloud point images with detailed annotations and labels for risk areas, safe areas, and other markings and attributes. This service is extensively used in robotics and autonomous vehicles for real-time detection of different areas of roads and adjacent areas.

Object Classification

The classification of different types of objects into certain categories, identities, or classes in an image, video, or a point cloud structure is known as object classification in the field of computer vision applications. This service also deals with the comparison of the attributes of previously calibrated objects with the attributes of new objects of the same category or class.

3D Point Cloud Semantic Segmentation

The detection and identification of a vast number of objects with the help of clouds of points presentation is known as point cloud semantic segmentation. In this service the data annotators populate different objects with a 3D cloud of points to detect and identify its types and other attributes. This is one of the most challenging services used in computer-vision projects.  

Why Is Sensor Fusion Critically Important to the Autonomous Vehicle Field?

Autonomous field is completely dependent upon the understanding of machines of the surroundings and environment in a real-time ecosystem. A small misunderstanding would lead to disastrous results. In such circumstances, the unification of multi-sensor data input and their combined effect is very critical for machines to build instantaneous understanding about the environment. Fusion of multiple sensors in driverless vehicles offers:

  • Accurate navigation – A combined set of sensor data enhances the accuracy of the machines to navigate through a wide range of objects, obstacles, lines, terrains, slopes, gradients, trees, and many kinds of other objects accurately without any mishaps at all.
  • Efficient decision making – A multilateral data input through sensor fusion improves the effectiveness of driving decisions of an automatic vehicle.
  • Increased safety – Multiple sensor fusion enhances the safety of driverless car and its travelers as well as the protection of other objects and vehicles on the roads greatly.
  • Improved performance – Fusion of multiple sensors, both internal and external, would improve the performance of the vehicles by avoiding any kinds of skidding and other balancing issues. The selection of the right position and location will also help improve fuel consumption efficiency.

To achieve the above-mentioned highly critical attributes in autonomous vehicles makes the sensor fusion an indispensable factor in this domain.

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An Introduction to Our 3D Annotation Staff Hiring Services

We are one of the most reliable and professional-grade providers of 3D sensor fusion annotation staff remotely. Our company is headquartered in Kyiv, Ukraine. We offer very unique services powered by numerous outstanding features such as:

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  • Quality of service – The quality of our services is par excellence. We use multiple professional channels and expertise to source, hire, and onboard the best sensor fusion experts in the marketplace.
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How Does Our Hiring Process for 3D Labeling Specialists Work?

Our hiring process for 3D labeling specialists is based on a few very simple steps such as:

  1. Just provide your requirements and desired objectives you want to achieve
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If you are looking to build a remote team for 3D labeling services, get in touch with us and hire a 3D sensor fusion annotation expert in Ukraine now!

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Svitlana, the insightful author behind Remote Labeler's blog, is an expert in the field of data annotation. With a wealth of experience, she brings a deep understanding of the intricacies involved in labeling data for machine learning models. Her expertise shines through in each post, offering valuable insights and practical knowledge that contribute to the evolving landscape of data annotation in the realm of remote labeling.
Svitlana Orlenko