Subul Data Annotation

Subul Data Annotation

Subul Data Annotation

Subul Data Annotation

3D Annotation

Elevate your models with precision 3D annotations. Tailored for diverse industries, our service enhances spatial accuracy in AI and computer vision applications.

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Defining 3D Annotation

Annotation in three-dimensional data, such as point clouds, incorporates depth, distance, and volume considerations. While cuboids are commonly utilized, alternative methods involve the use of voxels (3D pixels) for semantic segmentation.

Where is 3D Annotation Commonly Applied?

Medical: Analyze 3D scans, including CTs and MRIs, for detailed medical imaging.

Geospatial: Detect 3D structures on Synthetic-Aperture Radar imagery for precise environmental analysis.

Automotive: Track vehicles using LiDAR point cloud data for advanced driver-assistance systems.

Industrial: Identify anomalies on 3D scans of products for quality control in manufacturing.

Agriculture: Enable harvesting robots for fruit picking through annotated 3D data.

Retail: Detect gestures and poses for VR and AR applications, enhancing the shopping experience.

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Advantages & Disadvantages

Advantages:

  1. Rich Information from Additional Dimensions:

    • 3D annotation provides richer insights by considering additional dimensions, particularly valuable in challenging 2D conditions like fog or darkness.
  2. Adaptability to 2D Data:

    • Offers versatility by allowing 3D annotations to be approximated on 2D data, enabling tasks like segmentation of DICOM slices or cuboid estimation based on perspective.

Disadvantages:

  1. Time-Consuming Process:

    • 3D annotation is a time-intensive task, requiring advanced tools capable of efficiently handling large volumes of data.
  2. LiDAR Precision Challenges:

    • LiDAR data precision diminishes with distance, impacting annotation quality due to point dispersion as objects move away from the source.

3D Annotation Guidelines

  1. Leverage Multiple Viewpoints for Ease:

    • Use tools that offer visualization from multiple viewpoints (side, top, front) to simplify annotating a single object. Comparing 3D data to a 2D capture of the scene can serve as a sanity check.
  2. Smart Segmentation for Medical Imagery:

    • Utilize smart segmentation based on tissue density for 3D annotation in medical imagery. This ensures precise annotations with minimal effort.
  3. Maintain Consistent Annotation Position:

    • Create all 3D annotations in the same position concerning the pitch axis. This greatly simplifies labeling for the roll and yaw axes.
 
 
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Trusted Annotation Platforms

How Subul Data Annotation Works For You

Personalized and Fully Managed

Get a dedicated project manager to handle everything from guidelines to quality control, ensuring a seamless experience from start to finish.

Multilayer Quality Control

Experience the assurance of multi-tiered QC processes, including peer review and expert checks, guaranteeing a minimum accuracy of 98%.

Diverse and Scalable Workforce

Access a diverse team of industry experts, assembled within 72 hours, tailored to fit your project's specific needs and scale.

Unbiased and Compliant

Rely on our rigorously trained workforce for unbiased data, perfectly aligned with international privacy and AI standards.

Express Delivery

Benefit from our dynamic workforce, capable of rapidly scaling up to meet tight deadlines and ensure swift project delivery.

Direct Social Impact

Make a difference with every project; our entire workforce comes from refugee and disadvantaged backgrounds, contributing to social goals.

The world’s leading Ai teams Trust Us

Types of Our Data Annotation Serveries

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Frequently asked questions

What is the advantage of using 3D annotations over 2D annotations?

3D annotations offer additional dimensions, providing richer information, especially in challenging 2D conditions like fog or darkness.

How time-consuming is the 3D annotation process?

The 3D annotation process can be time-intensive, requiring advanced tools capable of efficiently handling large datasets.

Can 3D annotations be approximated on 2D data?

Yes, 3D annotations can be approximated on 2D data, allowing for tasks like segmentation of DICOM slices or cuboid estimation based on perspective.

What challenges are associated with LiDAR data in 3D annotations?

LiDAR data precision diminishes with distance, affecting annotation quality due to point dispersion as objects move away from the source.

How does smart segmentation aid in 3D annotation for medical imagery?

Smart segmentation based on tissue density facilitates precise annotations in medical imagery with minimal effort.

Why is it recommended to create 3D annotations in a consistent position concerning the pitch axis?

Consistent annotation positions concerning the pitch axis greatly simplify the labeling process for the roll and yaw axes.