Blending real and digital worlds.
XR Data Labeling
With our high-quality annotations, you can train robust XR models, spanning AR, VR, and mixed reality (MR), to deliver seamless, realistic, and immersive user interactions.
Need High-Quality XR Data Labeling?
Virtual and mixed reality applications demand precise, context-aware data to function seamlessly in dynamic environments. Our team specializes in labeling complex XR datasets, ensuring that every object, interaction, and spatial element is accurately annotated to enhance the performance of your immersive models.
Whether you need object detection, spatial mapping, gesture recognition, or full XR interaction data, we offer customized solutions that improve the quality and depth of your data. Our expertise ensures that your models can interpret real-world and virtual environments with precision, enhancing user experiences and system performance across virtual and mixed reality platforms.
From managing intricate 3D data to handling real-time labeling, we scale with your needs while maintaining rigorous quality control, ensuring reliable annotations for all immersive applications.
What is XR Data Labeling?
Extended Reality (XR) data labeling encompasses AR, VR, and MR, involving the annotation of real-world and virtual objects, scenes, and environments. This data allows computer vision algorithms to interpret and interact with both the physical and virtual worlds, creating immersive experiences.
Use Cases
XR data labeling is essential for applications such as object recognition and tracking, 3D mapping, scene understanding, and virtual object placement.
Object recognition in AR data labeling identifies and labels objects within augmented reality, enhancing retail, navigation, gaming, education, and more.
3D mapping creates spatial understanding, benefiting architecture, navigation, construction, and interior design applications.
Scene understanding enhances navigation, gaming, and advertising, providing context-rich experiences for users.
Virtual object placement enables seamless integration of digital objects into real-world environments, revolutionizing industries like interior design, marketing, and entertainment.
Techniques
XR data labeling techniques include 3D bounding boxes, semantic segmentation, depth estimation, and pose estimation.
3D bounding box annotation techniques involve labeling objects within a 3D space with precise geometric information, including their position, size, and orientation. This annotation is crucial for training AI models to understand object dimensions in a real-world context, enhancing applications like autonomous driving, robotics, and augmented reality. Annotators create 3D boxes around objects, specifying their coordinates and dimensions in 3D space. This data empowers AI systems to accurately perceive and interact with the physical world.
Semantic segmentation in AR data labeling involves labeling each pixel of an image with a corresponding class or category, allowing augmented reality applications to understand the objects and their boundaries within a scene. This technique enables precise object placement, occlusion handling, and interaction between virtual and real elements. Annotators identify and label different objects, such as surfaces, obstacles, and structures, in the image to create a pixel-wise understanding of the scene. This annotated data helps AR systems render virtual content seamlessly in real environments, enhancing user experiences and immersion.
Depth estimation in AR data labeling involves determining the distance of objects from the camera in an image, which is essential for creating realistic augmented reality experiences. Annotators use various techniques to assign depth values to objects, often in the form of a depth map. This data allows AR applications to accurately position virtual objects in a scene, accounting for their spatial relationships and occlusion by real-world objects. Depth estimation is crucial for achieving visual coherence between virtual and real elements, enhancing the perception of depth and realism in augmented reality environments.
Pose estimation in AR data labeling refers to the process of determining the position and orientation of objects or entities within a scene. This technique is crucial for creating accurate augmented reality experiences where virtual objects interact seamlessly with the real world. Annotators use pose estimation to track the position and orientation of objects relative to the camera's viewpoint. This information is vital for aligning virtual objects correctly in the scene, ensuring they appear in the correct location and orientation as the user moves or interacts with the AR application. Pose estimation plays a key role in enhancing the realism and immersion of augmented reality content.
Challenges
XR data labeling challenges include capturing accurate 3D data, handling occlusions and dynamic scenes, and ensuring virtual-real consistency.
Accurate 3D data capture can be complex due to factors like limited sensor accuracy, varying lighting conditions, and challenges in aligning the virtual and real worlds. Solutions include sensor fusion, adaptable algorithms, and precise calibration for optimal alignment.
Overcoming occlusions involves challenges like accurately representing objects blocked by others. Solutions include using depth information, predictive modeling, and refining algorithms to reconstruct occluded objects in augmented reality scenes.
Ensuring consistency between virtual and real-world annotations can be achieved through aligning reference points, implementing quality control measures, training annotators, and maintaining clear documentation. Using inter-annotator agreement tests and visual feedback to address alignment discrepancies and conduct regular audits to refine guidelines, and collaborating with annotators, reviewers, and AR experts to enhance accuracy can all contribute to maintaining consistency in AR data labeling.
Tools and Tech
XR data labeling often uses specialized tools like XR headsets, depth sensors, and point cloud processing software.
XR headsets are wearable devices equipped with displays, sensors, and processing units to overlay virtual elements onto the real world. They use cameras, tracking systems, and optics to blend real and digital content seamlessly. Examples include Microsoft HoloLens, Magic Leap, Apple Vision Pro, Varjo XR-4 series and devices from Meta.
Depth sensors are devices that measure the distance between the sensor and objects in the environment. They provide depth information to create 3D representations. Common examples are LiDAR sensors, structured light sensors, and time-of-flight sensors. They're used in AR to enhance spatial understanding, object placement, and interactions in mixed reality experiences.
Point cloud processing software is a tool used to analyze, manipulate, and visualize point cloud data collected from 3D scanning technologies like LiDAR or structured light. This software assists in converting raw data into meaningful 3D models, enabling accurate measurements, object recognition, and virtual object integration in AR applications. Popular examples include Autodesk ReCap, CloudCompare, and MeshLab.
What's Next
Discovery Call
We begin by thoroughly understanding your project goals, data requirements, and specific annotation needs. This detailed assessment allows us to tailor our approach precisely to your project’s unique specifications, ensuring accurate and effective results.
Scope Of Work
Our team collaborates closely with you to clearly define the project’s scope, establish realistic timelines, and outline key deliverables. This ensures that every aspect of the project is aligned with your expectations and that we meet your objectives efficiently and effectively.
Proposal
Receive your competitive quote and see how our services stand out. We are committed to demonstrating how we can surpass your current providers in terms of quality and value, ensuring that you get the best results for your investment.
Create immersive experiences with accurate AR data labels.
Shall We Have a Call?
The best way to embark on your annotation journey is by scheduling a free Discovery Call with us. In this brief 30-minute session, our experts will understand your project requirements, discuss your goals, and provide tailored guidance on the next steps.
Book your call today
And explore the possibilities of working together! It’s the first step towards unlocking the full potential of your data.
Articles
AR Data Labeling: Bridging the Physical and Digital Worlds
Augmented Reality (AR) blurs the lines between reality and digital worlds. AR data labeling involves annotating physical environments with digital information, allowing AR systems to understand and interact with real-world surroundings.