3D Imagery System for Seamless 360-Degree Video Stitching
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Solution Overview
Problem
Current 3D imagery systems are limited in capturing and displaying immersive 360-degree video with high resolution and real-time performance, often resulting in distorted or incomplete views, especially when viewers change their angle or perspective.
Innovation Solution
A 3D imagery system architecture that includes pairs of cameras configured to capture 360-degree video, with front-end processors for synchronization and encoding, a stitching server for image alignment and compression, and a content server for real-time streaming, allowing for dynamic adjustment of video frames based on viewer input to maintain a seamless and immersive experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple cameras are used to capture 360-degree video, then the field of view and immersion are improved, but the device complexity and image stitching difficulty increase
Solution Approach 1:
The 360-degree video capture is divided into multiple discrete camera views (typically 4-8 cameras arranged in a specific geometry). Each camera captures a portion of the spherical field of view, and the system processes each view separately through stabilization and rendering pipelines before combining them during stitching, making the complex task manageable through modular processing
Solution Approach 2:
The system transitions from traditional 2D planar video stabilization to 3D spherical coordinate system processing. By representing video frames in spherical coordinates (theta, phi) and performing stabilization operations in this 3D space, the system can handle the geometric complexities of 360-degree video while maintaining natural viewing experiences across all directions
2Reliability
If video stabilization is performed in real-time, then the viewing experience is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary stabilization calculations on reference frames before they are needed for rendering. By pre-computing stabilization transformations and storing them for later application, the system reduces real-time processing requirements while maintaining high-quality stabilization performance during actual video playback
Solution Approach 2:
The stabilization system dynamically adjusts its processing intensity based on scene content and user viewing behavior. When detecting smooth, predictable camera movements, the system applies lighter processing, while more complex or rapid movements trigger enhanced stabilization algorithms, optimizing the balance between quality and processing speed
3Measurement precision
If high resolution video is captured, then the image quality is improved, but the data compression and stitching accuracy become more difficult
Solution Approach 1:
The system applies different processing qualities to different regions of the 360-degree video based on user viewing probability. The equirectangular projection is divided into zones where the front-center region (most likely to be viewed) maintains full high-resolution quality, while peripheral regions use optimized compression. This localized quality adjustment preserves stitching accuracy in critical areas while reducing overall data requirements
Data Source
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AI summary
In one embodiment, a method includes receiving multiple images from multiple cameras, where the multiple cameras include a first camera having a first orientation and a second camera having a second orientation, and the multiple images include a first image from the first camera and a second image from the second camera. The method also includes detecting multiple feature points within the first and second images. The method further includes determining one or more pairs of corresponding feature points located within a region of overlap between the first and second images, where the pairs of corresponding feature points include a respective one of the feature points from each of the first and second images. The method also includes spatially adjusting the first or second image based on a calculated offset between each pair of corresponding feature points.