Machine Training With Laser Clarity
LIDAR (light detection and ranging) data equips machine learning models with the ability to sense and understand the 3D world around them.
Point clouds and 3D mapping enhance ML applications in a huge range of industries, sectors and academic disciplines, including:
- Environmental science
- Technology, e.g. driverless vehicles, UAVs, camera and imaging technologies
3D Data Annotation Services
LIDAR annotation converts complex 3D data into training datasets.
3D training data is essential to building sophisticated autonomous vehicles and ML models that need to make sense of complex urban and natural environments.
Aya Data are LIDAR labeling specialists.
We can help you build powerful 3D datasets to facilitate the training of next-gen 3D computer vision (CV) models.
LIDAR Labeling For Cutting Edge Models
Aya Data has built and deployed LIDAR datasets in cutting-edge scientific and commercial contexts where understanding the world from above is a priority - this is one of the most complex forms of data labeling and annotation.
By leveraging the very best LIDAR annotation tools, our team of professional annotators can deliver exceptional datasets for the most demanding projects.
3D LIDAR annotation is a complex task that requires skill, experience, and domain knowledge.
- We possess proven experience in LIDAR annotation.
- Our LIDAR annotation services draw upon the latest cutting-edge LIDAR labeling tools and the combined skills of our LIDAR data annotation specialists.
- We provide in-depth LIDAR annotation training to all team members.
3D Bounding Boxes
3D bounding boxes represent objects in three-dimensional space. When combined with LIDAR (Light Detection and Ranging) data, 3D bounding boxes can provide highly accurate object localization and recognition for complex ML applications, such as autonomous driving, robotics, and mapping.
3D bounding boxes encapsulate objects within a cuboid, taking into account their height, width, and length. These boxes provide valuable information about objects' position, size, and orientation.
3D polygons represent the surface of three-dimensional objects more accurately than bounding boxes.
A 3D polygon is a closed shape composed of multiple vertices connected by edges in a 3D space. Combined with LIDAR, 3D polygons can be employed to create a mesh representation of the point cloud.
3D Semantic Segmentation
3D semantic segmentation involves assigning semantic labels to every point within a 3D point cloud. Each point in the point cloud is assigned a semantic label, such as "car," "pedestrian," or "building."
This process enables the identification and understanding of individual objects and their parts in a three-dimensional scene.
3D semantic segmentation can deliver highly accurate and granular object recognition and classification for demanding applications.
What’s a Rich Text element?
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
Static and dynamic content editing
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
How to customize formatting for each rich text
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
- text for bullet points