Stereoscopic Panorama Synthesis Using Optical Flow Interpolation
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Solution Overview
Problem
Generating high-quality, artifact-free stereoscopic panoramas is challenging due to limitations in angular sampling and capture setup deviations, leading to visible seams, vertical parallax, and other artifacts, especially when capturing high-resolution images.
Innovation Solution
A system configured to generate stereoscopic panoramas using optical flow-based up-sampling and correction techniques, including orientation and distortion compensation, alignment, and structure-from-motion algorithms to interpolate and synthesize images, reducing memory consumption and enabling real-time interactive control over the stereoscopic depth effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If densely sampled images (significantly more than one image per degree) are captured to achieve artifact-free panoramas, then stitching quality and panorama seamless integration are improved, but capture time and computational cost increase significantly
Solution Approach 1:
The system performs preliminary action by capturing a sparse set of key images and then using optical flow computation and image interpolation to synthesize the dense sampling required for artifact-free stitching. This preliminary capture of essential images followed by computational synthesis resolves the contradiction by reducing actual capture time while maintaining high stitching quality through algorithmic generation of intermediate images.
Solution Approach 2:
The system creates copies of image information through optical flow-based interpolation, generating synthetic intermediate images that replicate the appearance and content of densely captured images. This copying approach allows the system to achieve dense sampling effects without physically capturing every intermediate image, thus improving capture efficiency while maintaining stitching quality.
2Measurement precision
If thousands of high-definition images are captured to achieve high-resolution panoramas, then output resolution and image quality are improved, but memory consumption and processing requirements become prohibitively large
Solution Approach 1:
The system performs preliminary computation of optical flow fields between adjacent captured images, storing only these compact flow representations rather than all intermediate high-resolution images. This preliminary action enables high-resolution panorama generation on-demand through interpolation, dramatically reducing memory consumption while maintaining output resolution quality.
Solution Approach 2:
The system changes the parameter representation from storing full high-resolution images to storing compact optical flow vector fields. This parameter transformation allows the system to achieve high output resolution through computational interpolation of flow fields, reducing memory requirements from gigabytes to manageable sizes while preserving image quality.
3Manufacturing precision
If correction techniques are applied to compensate for capture setup deviations, then panorama accuracy and artifact reduction are improved, but processing complexity and computational load increase
Solution Approach 1:
The system merges multiple correction operations (distortion correction, orientation alignment, vertical parallax compensation) into a unified processing pipeline that operates on optical flow fields. By combining these corrections into a single integrated process rather than separate sequential steps, the system improves panorama accuracy while reducing overall processing complexity and computational overhead.
Solution Approach 2:
The system uses optical flow fields as intermediate copies that encode all necessary geometric and photometric information for correction. These flow field copies serve as a compact representation that simplifies subsequent correction operations, reducing processing complexity compared to working directly with raw pixel data while maintaining high panorama accuracy.
Data Source
AI summary
Systems and methods to generate stereoscopic panoramas obtain images based on captured images. The obtained images may be processed and/or preprocessed, for example to compensate for perspective distortion caused by the non-ideal camera orientation during capturing, to reduce vertical parallax, to align adjacent images, and/or to reduce rotational and/or positional drift between adjacent images. The obtained images may be used for interpolating virtual in-between images on the fly to reduce visible artifacts in the resulting panorama. Obtained and/or interpolated images (or image fragments) may be stitched together to form a stereoscopic panorama.