Physical Object Orientation from Multi-Camera Local AI Processing
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Solution Overview
Problem
Existing systems for reconstructing three-dimensional shapes of physical objects face network bottlenecks due to high data transmission and alignment issues when comparing reconstructed models with reference models, particularly when the object's orientation is unknown.
Innovation Solution
A method using multiple spatially distributed cameras with machine learning modules to determine object orientation, where images are captured simultaneously, processed locally, and combined by a central processing unit to minimize network latency and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all captured images are transmitted to a central processing unit for reconstruction, then the object shape can be reconstructed, but network bottlenecks occur due to high data transmission requirements
Solution Approach 1:
The system divides the centralized processing task into distributed processing units, where each camera is associated with a separate computing system. Each computing system independently processes images from its associated camera to determine orientation estimates, thereby segmenting the large data transmission task into smaller, manageable units that only transmit essential orientation data rather than full images.
Solution Approach 2:
The invention extracts only the essential information (orientation estimates) from the captured images at the distributed computing systems, rather than transmitting the complete image data to the central processing unit. This extraction principle reduces network transmission volume while preserving the necessary information for object shape reconstruction.
2Measurement precision
If multiple cameras capture images simultaneously from different perspectives, then orientation determination accuracy improves, but network latency increases due to data transmission
Solution Approach 1:
The distributed computing systems perform preliminary processing of images locally immediately after capture, determining orientation estimates before transmission to the central processing unit. This preliminary action reduces network latency by preparing data in advance at the source, eliminating the need to transmit raw images for processing.
Solution Approach 2:
The distributed computing systems act as intermediaries between the cameras and the central processing unit. They receive images from cameras, process them locally to extract orientation information, and then transmit only the processed results to the central unit, thereby reducing network latency and transmission burden.
3Adaptability or versatility
If the object is positioned in free fall for unbiased capture, then complete object surfaces are visible, but alignment with reference models becomes problematic due to unknown orientation
Solution Approach 1:
The system uses machine learning modules to analyze images from multiple cameras and generate orientation estimates as feedback about the object's position. This feedback mechanism enables the system to determine the object's orientation even when captured in free fall, allowing subsequent alignment with reference models despite the unknown initial orientation.
Solution Approach 2:
The invention changes the approach from assuming known orientation to determining orientation as a variable parameter through machine learning analysis. By treating orientation as a parameter to be estimated from multiple camera views rather than a known quantity, the system can handle free-fall conditions while maintaining alignment accuracy.
Data Source
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AI summary
The present invention relates to a method for determining the orientation of a physical object in space. The method determines the orientation of the object from the images captured by a set of cameras distributed around an image capture space. The method is characterized by a set of steps which comprise: capturing multiple images of the physical object in the same time instant, machine learning modules determining the estimate of the orientation of the physical object for each of the cameras using the captured images, and a central processing unit determining the orientation of the physical object with respect to a global reference by combining the different estimated orientations.