Cloud Camera Calibration Offloading for VR
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Solution Overview
Problem
Existing methods for calibrating cameras in social VR environments are complex and require significant processing power, often necessitating specialized hardware, which can be a challenge when local processing capabilities are limited and network delays are a concern, especially in real-time applications like self-view rendering in virtual reality.
Innovation Solution
A processor system that offloads complex calibration tasks to a remote server, using image data and calibration instructions to generate calibration data, which is then used for local processing of 3D object data, reducing the processing load on the client and minimizing delays by leveraging remote processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If camera calibration is performed locally using specialized hardware (GPU/FPGA), then processing speed and real-time performance are improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the calibration processing task from the local device and moves it to a remote server. The local device only captures images and transmits them to the server, which performs the complex calibration computations using its own processing resources. This extraction eliminates the need for specialized GPU/FPGA hardware on the local device while maintaining real-time calibration capability through network communication.
Solution Approach 2:
The patent introduces a server as an intermediary between the local camera system and the calibration process. The server receives image data from the local device, performs calibration using its processing resources, and returns calibration parameters to the local device. This intermediary approach allows the local device to remain simple while achieving fast calibration through the server's computational power.
2Device complexity
If camera calibration is performed remotely on a server, then device complexity is reduced, but processing time and network latency increase
Solution Approach 1:
The patent implements preliminary calibration actions by having the server pre-process and analyze calibration images before actual use. The server performs calibration computations in advance using transmitted images, and the resulting calibration parameters are ready for immediate application. This preliminary processing reduces the time needed during actual operation, as the heavy computational burden is completed beforehand.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining a continuous workflow where images are continuously captured, transmitted to the server for calibration processing, and calibration parameters are continuously updated. This continuous cycle minimizes idle time and ensures that calibration is always current, reducing overall processing time despite the remote location of the server.
3Measurement precision
If multiple cameras are calibrated using automated point matching, then measurement precision is improved, but processing complexity and time increase
Solution Approach 1:
The patent extracts the complex automated point matching and calibration computations from the local device and relocates them to a remote server. The local device simply captures images from multiple cameras and transmits them to the server, which then performs the complex alignment and calibration calculations using its processing resources. This extraction maintains high measurement precision while significantly reducing the complexity of the local calibration process.
Solution Approach 2:
The patent uses image data copies transmitted from the local device to the server for calibration processing. Instead of performing complex processing on the original images at the local device, the server works with copies of the image data, allowing sophisticated calibration algorithms to be applied without burdening the local device. This copying approach maintains measurement precision while simplifying the local system architecture.
Data Source
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AI summary
A processor system (200) processes image data from a camera (20) to render a virtual object in a 3D virtual environment and communicate via a network to a server system (300). A processor (220) obtains image data of the physical space from the camera and second data of a second device (30). The processor system sends the image data, the second data and a calibration command to the server system. In the server, a processing resource (320) processes the data according to the calibration command and sends calibration data indicative of a camera pose and a second pose of the second device. The processor generates 3D object data of the object by processing the image data based on the calibration data. The 3D object data is provided for rendering the virtual object in the 3D virtual environment. So, the processor is in control, while the calibration is executed by the server.