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On-Device Machine Learning in Spatial Computing
- The article discusses creating an app that demonstrates the capabilities of on-device machine learning in visionOS by recognizing and tracking a diet soda can in real-time.
- The app leverages technologies in the visionOS ecosystem to detect diet soda cans in the user's environment, displaying bounding lines and text labels for tracking.
- The development process involves capturing a detailed 3D model of the target object using RealityComposer and training the recognition model with Create ML.
- Training the model involves importing the 3D model, defining recognition parameters, and undergoing a training phase to recognize objects in real-time.
- The output of the training process is a .referenceobject file optimized for real-time object detection in visionOS.
- The article guides through setting up a visionOS project in Xcode, configuring permissions for object tracking, and implementing object tracking functionality.
- The core implementation includes initializing object detection systems, setting up ARKit sessions for tracking, and managing real-time object detection using ObjectTrackingProvider.
- ImmersiveView handles real-time object detection visualization by processing detection data and creating representations of detected objects.
- ObjectVisualization is responsible for creating and managing the bounding box and text overlay for detected objects to provide visual feedback.
- The application provides users with a complete object detection experience, combining 3D object recognition, real-time tracking, and user interface components.
- The article concludes by highlighting the potential of on-device machine learning in spatial computing for advanced AR experiences and intelligent environmental understanding.
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