Multi-Camera Image Stitching with Predicted Disparity
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Solution Overview
Problem
Existing image stitching technologies face challenges in efficiently combining images from multiple cameras with overlapping fields of view, particularly in reducing temporal and motion artifacts, and achieving low-latency, high-quality stitching for wide-view content like 360-degree videos.
Innovation Solution
The method predicts the disparity of moving objects using depth detection and object tracking, determines warp vectors based on predicted disparities, and applies dynamic warping to combine images from multiple cameras, adapting filtering techniques to minimize artifacts and optimize computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex processing methods are used to expand the view and reduce artifacts, then image quality is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by predicting the future position of moving objects before they enter the overlapping region. Depth detection and object tracking are used in advance to estimate where objects will be, allowing the stitching algorithm to pre-compute warp vectors and prepare blending operations. This proactive approach reduces the computational burden during actual stitching, improving both quality and speed.
Solution Approach 2:
The system dynamically adapts the stitching process based on detected motion. Instead of using a static stitching approach, the system continuously updates warp vectors based on real-time object tracking and depth information. This dynamic adaptation allows the system to focus computational resources on regions with moving objects, reducing overall processing time while maintaining high image quality in critical areas.
2Manufacturing precision
If complex processing methods are used to expand the view and reduce artifacts, then image quality is improved, but computational resources increase
Solution Approach 1:
The system applies local quality by treating different regions of the stitched image differently. Instead of applying uniform complex processing to the entire image, the system identifies regions containing moving objects and applies sophisticated warp vector computation only to those areas. Background regions with no motion are processed using simpler, faster methods, significantly reducing overall computational resource requirements while maintaining high quality where it matters most.
Solution Approach 2:
The system extracts moving objects from the scene using object detection and tracking algorithms. By separating moving objects from the static background, the system can apply different processing strategies to each. The extracted moving objects receive intensive processing for accurate alignment and blending, while the background is processed more efficiently, reducing total computational load.
3Device complexity
If standard image stitching is used, then processing is simpler, but temporal and motion artifacts increase
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring the positions of moving objects across multiple frames using object tracking. This feedback information is fed back into the stitching algorithm to dynamically adjust warp vectors and blending parameters. The feedback loop ensures that temporal artifacts are minimized by maintaining consistency between consecutive frames, improving reliability without requiring excessively complex processing.
Solution Approach 2:
The system performs preliminary depth detection and object tracking to predict future object positions before stitching occurs. This preliminary action allows the algorithm to pre-calculate appropriate warp vectors that account for motion, preventing temporal artifacts like ghosting and flickering. By preparing these corrections in advance, the system achieves better temporal consistency without adding excessive complexity to the main stitching process.
Data Source
AI summary
A method performed by an electronic device is described. The method includes receiving a plurality of images from a first camera with a first field of view and a second plurality of images from a second camera with a second field of view. An overlapping region exists between the first field of view and the second field of view. The method also includes predicting a disparity of a moving object present in a first image of the first plurality of images. The moving object is not present in a corresponding second image of the second plurality of images. The method further includes determining warp vectors based on the predicted disparity. The method additionally includes combining an image from the first plurality of images with an image from the second plurality of images based on the determined warp vectors.