Multi-Camera Object Orientation to Reduce Image Data Bottlenecks
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Solution Overview
Problem
Existing systems for reconstructing three-dimensional shapes of physical objects from photographic images face network bottlenecks due to the large amount of information transmitted to a central processing unit, and alignment issues arise when the orientation of the object is unknown, complicating comparisons with reference models.
Innovation Solution
A method using a distributed processing system with multiple cameras, each with a machine learning module, captures images simultaneously from different perspectives, and a central processing unit combines these estimates to determine the object's orientation relative to a global reference, reducing network latency and improving 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 the large amount of information transmitted
Solution Approach 1:
The system divides the central processing task into distributed processing units, each associated with one or more cameras. Each distributed unit independently processes images from its associated cameras to extract orientation information, rather than transmitting all raw images to a single central unit. This segmentation reduces the data transmission volume while maintaining reconstruction accuracy.
Solution Approach 2:
The system extracts only the essential orientation information from the captured images at the distributed processing level, rather than transmitting the complete image data to the central unit. This extraction principle reduces the quantity of transmitted data to only the critical parameters needed for reconstruction and orientation determination.
2Measurement precision
If images are processed in a centralized system, then reconstruction can be performed, but network latency increases due to the large amount of data transmission
Solution Approach 1:
The processing system is segmented into distributed units that perform orientation estimation locally near the cameras. This eliminates the need to transmit large volumes of raw image data across the network, significantly reducing network latency while maintaining the ability to determine object orientation accurately through centralized combination of the distributed estimates.
3Ease of operation
If the object orientation is unknown, then the object can be captured in free fall, but alignment issues arise when comparing the reconstruction with the reference model
Solution Approach 1:
The system performs preliminary orientation estimation for each camera view before the final reconstruction step. Each distributed processing unit estimates the object orientation from images captured by its associated camera, and these preliminary estimates are then combined by the central processing unit to determine the global object orientation. This preliminary action enables accurate alignment with reference models while maintaining the simplicity of free-fall capture.
Solution Approach 2:
The system uses the orientation estimates from multiple distributed cameras as feedback to determine the global object orientation. By combining the different orientation estimates from multiple perspectives, the system achieves accurate orientation determination that enables precise alignment with reference models, while the object remains in simple free-fall capture.
Data Source
AI summary
A method for determining the orientation of a physical object in space which determines the orientation of the object from the images captured by a set of cameras distributed around an image capture space. The method includes a set of steps which comprise: capturing multiple images of the physical object at 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.