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As AI (Artificial Intelligence) undergoes rapid development, the demand for utilizing 3D data is increasing. Against this backdrop, "3D annotation" is attracting significant attention.
3D annotation is the process of labeling or tagging 3D data so that AI models can understand shape and spatial information. To supplement depth and structural information where traditional 2D annotation had limits, 3D annotation has become an indispensable technology in a wide range of fields such as robotics and autonomous driving.
On the other hand, many people may have questions like "What are the specific methods and types of 3D annotation?"
In this article, we will explain the basic concepts and types of 3D annotation, and why its importance is currently increasing. We will also touch upon use cases and current challenges.
By reading to the end, you will understand the basics to applications of 3D annotation and gain the knowledge necessary for AI model development and business implementation.
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【Table of Contents】 |
1. What is 3D Annotation?
3D annotation is an annotation technology that labels information such as the position, shape, and characteristics of objects on 3D models, point cloud data, or 3D scan data.
A key feature is that, unlike traditional 2D, information regarding design and manufacturing—such as spatial relationships and object details—can be conveyed more clearly through a mechanism that displays labels directly on 3D models.
For example, if 3D annotation data is used in mechanical design, dimensions, materials, and processing methods of parts can be shared visually, facilitating smooth communication between designers.
3D annotation is expected to be utilized in various fields such as architecture, manufacturing, autonomous driving technology, and AR/VR development.
What is Point Cloud Data and LiDAR?
To deeply understand 3D annotation, it is important to grasp the basic concepts of "point cloud data" and "LiDAR."
First, point cloud data is a data format that represents objects or environments in 3D space as a collection of points. It usually contains millions to billions of points, and each point includes information such as the following:
X/Y/Z Coordinate Information:
Data indicating the position of each point in three-dimensional space.
Attribute Information:
Data representing the characteristics of objects, such as color and reflection intensity.
Point cloud data serves as the foundation for creating high-precision 3D models. Particularly with the advancement of deep learning technology, the accuracy and efficiency of processing point cloud data have improved. Point cloud data is utilized in a wide range of fields such as autonomous driving, robotics, and architectural modeling.
And one method of acquiring point cloud data is "LiDAR." LiDAR is a sensor technology that calculates distance by emitting laser light toward a target and measuring the reflection time.
By using LiDAR, dense and accurate 3D point cloud data can be acquired, allowing for detailed understanding of the shape and position of objects. LiDAR is an indispensable technology in fields requiring high-precision environmental data, such as autonomous driving technology, drone surveying, and infrastructure inspection.
Types of Methods
The methods for performing 3D annotation include the following types:
Bounding Box:
Uses rectangular cuboid bounding boxes to perform object localization and classification. Used for things like surrounding environment recognition for autonomous vehicles, it has a low computational load and is easy to implement even in small-scale systems.
Semantic Segmentation:
Assigns specific class labels (e.g., ground, vegetation, building, etc.) to each point or region within a space.
Instance Segmentation:
Labeling to identify each object individually. For example, in a point cloud with multiple cars, each vehicle is labeled as an individual instance.
Object Detection:
Labeling points belonging to a specific object. For example, it is used to identify cars, trees, buildings, etc.
These methods are used differently depending on the purpose of annotation and the characteristics of the data.
2. Use Cases of 3D Annotation
3D annotation is being utilized in a wide range of fields such as autonomous driving, robotics, and medical sectors. Here, we introduce use cases for 3D annotation.
Autonomous Driving
In the field of autonomous driving, 3D annotation is utilized to officially recognize surrounding objects and the environment.
For example, by annotating recognized objects such as vehicles, pedestrians, and traffic lights existing within the 3D space surrounding a vehicle, it contributes to enhancing the decision-making capabilities of self-driving cars. 3D annotation technology is applied to collision avoidance and path planning, strengthening the safety of autonomous driving.
Robotics
In the field of robotics as well, 3D annotation has diverse applications.
For example, it is useful for acquiring information regarding the position and shape of objects necessary when a robot grasps an object. It can also be utilized as basic data for robots to recognize and avoid obstacles during operation or to move while understanding the surrounding environment.
In this way, 3D annotation data is utilized in robot motion planning, contributing to the improvement of robot efficiency and safety.
Medical Field
In the medical field, 3D annotation is utilized for the analysis of CT scans and MRI images.
Specifically, it is used to label organs or lesion sites appearing in MRI images, serving as auxiliary data when medical professionals perform diagnoses. Furthermore, if applied to automatic recognition of organs/abnormal sites or AI-based diagnostic support technology, improvements in diagnostic efficiency and accuracy are expected.
AR/VR Content Production
In the fields of AR and VR as well, 3D annotation is indispensable.
For example, it enables the tracking of object placement and movement in virtual environments and is utilized in the design of user interactions. It also has the effect of making the integration of virtual space and the real world smoother, further increasing the user's sense of immersion.
Therefore, 3D annotation is utilized for improving the quality of a wide variety of AR/VR content, such as games, education, and training simulations.
3. Methods for Performing 3D Annotation
Here, we introduce two methods for actually performing 3D annotation. Both methods have merits and demerits, and a choice should be made based on the project scale and purpose.
Requesting an Annotation Agency Service
To perform high-precision 3D annotation, the method of using an annotation agency service is optimal.
By using an agency service, high-quality annotation by specialized staff can be expected. Specifically, even with data containing a large number of objects and complex shapes, such as forests or cities, accurate annotation data can be obtained in a short time.
On the other hand, many representatives seem concerned about the high cost relative to receiving high-precision annotation. It is true that 3D annotation tends to have a higher unit price compared to text or audio data. Nevertheless, in many cases, considering the personnel and education costs incurred during in-house production, using an agency service is often more advantageous in terms of overall cost.
Nextremer provides 3D annotation services to realize high-precision AI models. If you are considering outsourcing 3D annotation even slightly, we offer free consultations, so please feel free to contact us at any time.
Performing In-house Using Annotation Tools
Another method is to bring production in-house by using specialized 3D annotation tools.
The merit of in-house production is that progress and quality can be managed directly within the company. Adjustments can be made flexibly according to the project's progress, responding finely to needs such as design or development.
However, many 3D annotation tools are limited to general-purpose use. Therefore, if your company's needs are specialized, you may not find a suitable tool, or man-hours may be taken up by adjusting settings.
Using annotation tools is an effective means when performing relatively simple annotations.
4. Challenges in Performing 3D Annotation
Because 3D annotation includes detailed information such as spatial relationships, it faces many challenges, starting with the difficulty of annotation itself. Here, we introduce challenges in 3D annotation and their solutions.
Enormous Volume of Data
The greatest challenge in 3D annotation is the sheer volume of data.
3D data can consist of tens of thousands to millions—or in large-scale cases, billions—of points, requiring vast resources for processing and storage. For example, because LiDAR scans and high-resolution 3D models contain many data points, the scale of the annotation work itself is very large, leading to increased time and costs for annotation.
To address this efficiency issue in 3D annotation, some companies are utilizing AI tools. AI tools for automatic sorting of 3D point cloud data and automatic labeling tools are provided by some vendors. However, at present, AI tool accuracy is unstable—such as requiring additional training depending on the application field—and the options for usable tools are limited.
Therefore, if reliable and high-quality annotation is required, it is more certain to utilize a specialized annotation agency service.
High Technical Expertise for Annotation
Because 3D annotation requires advanced technical knowledge and expertise, the human resources capable of handling the work are limited. In particular, handling point cloud data and 3D models requires the use of specialized tools and a deep understanding of data structures.
However, since training skilled annotators in-house takes significant time and cost, aiming for efficiency in annotation work is not a simple matter.
To resolve these efficiency challenges when it is difficult to handle in-house, outsourcing to an annotation agency service is a potential solution. By utilizing an agency service, annotators with specialized knowledge of annotation perform the work, making it possible to reduce the internal burden while ensuring quality.
Difficulty of Annotating Dynamic Data
Objects contained in point cloud data are often partially hidden or have ambiguous boundaries, making them difficult to annotate accurately.
Particularly in dynamic scenes, such as situations where vehicles or pedestrians are moving, the position and shape of objects change over time, making annotation more difficult than with static data. Advanced technology and specialized knowledge are required to accurately track continuous changes along a time axis and assign appropriate labels.
To resolve challenges when handling dynamic data, the introduction of specialized tools or the use of specialized agency services is effective. Especially when accurate labeling is required, utilizing agency services that specialize in dynamic data is recommended.
5. Summary
3D annotation is an essential task for supporting accuracy improvements in the latest fields such as autonomous driving, robotics, and AI. It enables advanced data processing and analysis of 3D models, point cloud data, and 3D scan data, contributing to the development of design and manufacturing processes.
On the other hand, because 3D annotation has fewer automation tools compared to other annotation tasks and requires specialized knowledge and technology, it is not easy to bring production in-house.
Therefore, if you desire high-precision and efficient 3D annotation, using a specialized annotation agency service is effective. An annotation agency service company can accurately annotate even point cloud data of complexly intertwined terrain that is difficult to label.
Let's leverage 3D annotation data to accelerate business in the latest fields, starting with autonomous driving and robotics.
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