AR Point Cloud Mapping for Large-Scale Mobile Rendering
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Solution Overview
Problem
Existing AR systems face challenges in generating and efficiently displaying large-scale point clouds due to the computational demands and logistical difficulties in data collection, making it impractical for use in mobile devices.
Innovation Solution
An AR system that generates point clouds using data from omnidirectional cameras, applies transformations, and loads optimized portions based on contextual conditions such as location, time, device attributes, and network connectivity to efficiently display AR content on client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If point clouds are generated for large-scale environments, then AR content coverage is improved, but computational demands and data storage requirements increase significantly
Solution Approach 1:
The patent divides large-scale point cloud data into multiple smaller chunks or segments that can be individually managed and loaded. This segmentation allows the system to handle large environments by processing only relevant portions at any given time, reducing the computational burden on mobile devices while maintaining comprehensive AR coverage across the entire large-scale area.
2Loss of information
If complete point clouds are loaded for large environments, then AR display completeness is improved, but memory and processing requirements exceed mobile device capabilities
Solution Approach 1:
The patent implements local quality by loading and processing only the specific portions of point cloud data that are relevant to the user's current location and field of view. This approach ensures that the AR display maintains completeness for the visible area while avoiding the memory and processing requirements of loading entire large-scale point clouds, thus adapting data volume to mobile device capabilities.
3Measurement precision
If point cloud data is collected comprehensively for large areas, then AR accuracy is improved, but data collection time and logistical complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing point cloud data into manageable segments during off-peak times or using distributed data collection methods. This preliminary preparation allows the system to quickly load and process accurate AR data when needed, improving AR accuracy without requiring lengthy real-time data collection that would delay deployment.
4Productivity
If optimized portions of point clouds are loaded based on contextual conditions, then device performance is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic loading strategies where the system adaptively adjusts which portions of point cloud data are loaded based on real-time contextual conditions such as user location, device performance metrics, network availability, and environmental factors. This dynamic approach improves AR rendering efficiency by loading only necessary data while managing system complexity through adaptive algorithms that respond to changing conditions rather than requiring static preconfiguration.
Data Source
AI summary
An Augmented-Reality which performs operations that include: accessing a data object that comprises image data, location data, and orientation data; applying a transformation to the data object to produce a rectified data object; generating a point cloud based on the rectified data object; assigning the point cloud to a location based on at least the location data of the data object; detecting a client device at the location; and loading the point cloud to the client device in response to the detecting the client device at the location.