Deep Learning VR Image Rectification
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Solution Overview
Problem
Existing VR image rectification technologies are inconvenient, time-consuming, and limited in application, particularly for hand-held cameras and non-urban environments, as they often require manual intervention, additional hardware, or are restricted to environments with man-made structures.
Innovation Solution
A deep learning method and apparatus using a convolutional neural network (CNN) to automatically estimate the orientation of VR images and apply rotations to achieve upright rectification, capable of processing various environments, including urban and non-urban scenes, without requiring additional hardware or specific metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual rectification methods are used, then rectification accuracy can be maintained, but the process becomes time-consuming and inconvenient
Solution Approach 1:
The patent replaces manual mechanical rectification operations with an automated deep learning system. A convolutional neural network (CNN) automatically detects orientation information and applies corrections to VR images, eliminating the need for manual intervention while maintaining high rectification accuracy and reducing processing time to approximately 100 milliseconds.
2Measurement precision
If additional hardware or specific metadata is required for rectification, then orientation detection accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent enables the VR image processing system to self-determine its orientation by analyzing the visual content of the images themselves. The deep learning model extracts orientation information directly from the image data without requiring external sensors, gyroscopes, or metadata, making the system self-sufficient and applicable to any VR camera regardless of additional hardware.
Solution Approach 2:
The deep learning-based rectification system is designed to be universally applicable to all VR images regardless of the camera device used. By relying solely on image content analysis rather than device-specific hardware or metadata, the system can process images from any VR camera, including hand-held devices without specialized orientation sensors.
3Productivity
If existing rectification methods are used, then processing speed can be maintained, but applicability to diverse environments and hand-held cameras is limited
Solution Approach 1:
The patent transforms the rectification approach by changing from hardware-dependent parameters to learning-based parameters. The deep learning model is trained on diverse datasets representing various environments and camera orientations, enabling it to adapt to different scenarios. This allows the system to maintain fast processing speeds while achieving broad environmental adaptability and compatibility with hand-held cameras.
Data Source
AI summary
Proposed are a deep learning method and apparatus for the automatic upright rectification of VR content. The deep learning method for the automatic upright rectification of VR content according to an embodiment may include inputting a VR image, to a neural network and outputting orientation information of the VR image through a trained neural network.