Systems that involve face recognition, computer vision, and more commonly use image annotation. It is of further two types: 2D and 3D annotation. 2D data refers to images and videos. 3D data includes point clouds and meshes. Data from cameras, ion, electron, or scanning probe microscopes also comes under the category of 3D data. AI models can perform 2D and 3D annotation because they extensively train on large amounts of data. This training helps these models identify certain patterns based on which they perform the process of 3D annotation. If you are on the hunt to source quality data sets to train your 3D annotation models then do check out Expert Data Annotation. Their in-house experts curate the best quality data sets to optimize your AI models.
Annotators label image data with notes or metadata to help AI models understand it. In this blog, we’ll discuss in-depth 3D annotation along with the commonly used 3D annotation techniques. Keep reading!
How is 3D Annotation Different from 2D Annotation?
In the general concept of image annotation, training of the models is necessary as it makes the model capable of recognizing and interpreting objects, structures, and patterns in the data. In the case of 2D data, the primary task is to draw boundary boxes or shapes around the objects so that some specific features can be identified and tagged. This task may be done manually by the annotators.
On the contrary, the process of 3D data annotation involves handling point clouds and meshes along with volumetric data captured by sensors. Techniques like photogrammetry and Lidars produce a collection of points described by x, y, and z coordinates, which we call point clouds. 3D meshes describe the surface of an object by joining points through polygonal shapes in 3D space. Professionals commonly use such meshes in the medical and gaming industries.
In 3D annotation, annotators segment different parts of a scene to identify the objects present in a scene with the help of cuboids. We do this so that the 3D annotation model can understand the spatial relationship between different elements. For instance, an ADAS system analyzes the objects on the road and the surroundings with the help of 3D annotation.
3D Annotation Techniques
- Cuboid Labeling
In this process, objects are labeled with the help of 3D boxes or cuboids. They are similar to bounding boxes used in 2D annotation but it must be noted that cuboids provide much more accurate information about the volume, orientation, and position of the object. For instance, in an ADAS system, objects on the road, people, sidewalks, and more are highlighted with the help of cuboids. Apart from highlighting the objects, 3D annotation also helps in identifying the space occupied by objects in the space.
- Point Cloud Segmentation
Point cloud segmentation involves labeling every data point in a point cloud according to its class. A point cloud represents an object’s surface in three dimensions, unlike 2D images composed of pixels. In the process of segmentation, these pointers are categorized into multiple classes. For example, buildings, humans, trees, vehicles, roads, sidewalks, and more in ADAS captured data.
Every point in the cloud has a different label, allowing the model to differentiate between various objects and understand their shape and position. Researchers segment the point cloud using instance or semantic segmentation methods.
- Sensor Fusion Annotation
The sensor fusion annotation technique plays an important role in 3D annotation. This technique combines data from multiple sensors to obtain a comprehensive view of the image’s environment. Using this fused data, annotators create detailed labels that incorporate information from each sensor. This technique can increase the model’s ability to understand complex scenes. However, we must note that labeling 3D data is relatively difficult since it requires adding an additional dimension.
How Expert Data Annotation Can Help
So was a detailed guide about 3D annotation. In the present time, AI & ML are evolving at a high pace and if you want your business to grow, you have to integrate AI into your organization. Check out Expert Data Annotation, we are your go-to AI partners as we provide the best 3D annotation datasets in the entire market.
With Expert Data Annotation, you get outstanding quality, scalability, expertise, and support. We follow ethical methods to compile data sets that’ll take your AI systems to a whole new height. Ready to elevate your 3D annotation models?
FAQs
Ans: – 3D annotation involves labeling 3D data like point clouds and meshes so AI models can understand spatial relationships between objects in a three-dimensional space.
Ans: – When dealing with 2D data, the primary task involves drawing boundary boxes or shapes around the object to identify and tag specific features, but when working with 3D data, the main task focuses on handling point clouds, meshes, and volumetric data obtained from sensors.
Ans: – Key techniques include cuboid labeling, point cloud segmentation, and sensor fusion annotation.
Ans: – Industries like autonomous driving, robotics, gaming, AR/VR, and healthcare benefit from 3D annotation for spatial awareness and analysis.
It’s challenging due to the complexity of working with 3D data and the precision required to label objects in three-dimensional space.
Ans: – Reach out to us as we offer high-quality 3D annotation datasets. Expertly labeled to enhance AI models for various applications, with scalability and tailored services.
