Multi-View Interactive Media Guidance via IMU and Image Fusion
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Solution Overview
Problem
Current augmented reality technologies face challenges in efficiently generating and implementing three-dimensional (3D) information for dynamic scenes, as the process of creating 3D reconstructions is computationally expensive and typically restricted to static environments.
Innovation Solution
The system utilizes inertial measurement units (IMUs) and image data to generate views of synthetic objects, allowing for the creation of immersive surround views by fusing 2D and 3D data, and providing interactive digital media representations that can model moving scenery and objects, while reducing computational costs through efficient data compression and stabilization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D reconstruction is used to add three-dimensional information to video and image data, then the quality and immersion of augmented reality is improved, but the computational cost and processing time increase significantly
Solution Approach 1:
The system segments the scene into static background elements and dynamic foreground objects. 3D reconstruction is applied only to static elements using multi-view geometry, while dynamic objects are handled through video processing and motion estimation. This division reduces the overall computational burden while maintaining visual quality and immersion in the augmented reality experience.
Solution Approach 2:
The system transitions from static 3D reconstruction to a dynamic approach where the scene is continuously updated based on camera motion and object movement. By using structure-from-motion algorithms and tracking dynamic objects across frames, the system adapts the level of 3D processing to the actual scene complexity, reducing unnecessary computational overhead while maintaining reliability.
2Adaptability or versatility
If 3D reconstruction is performed on dynamic scenes, then the applicability of augmented reality to moving scenery is improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The system employs dynamic scene understanding through continuous tracking of moving objects and camera pose estimation. By distinguishing between static and dynamic elements in real-time and applying appropriate processing pipelines, the system achieves versatility in handling diverse scenes without requiring full 3D reconstruction of all elements, thus managing computational complexity effectively.
Solution Approach 2:
The system replaces traditional mechanical 3D reconstruction pipelines with computational approaches based on multi-view geometry and structure-from-motion algorithms. These computational methods automatically infer 3D structure from 2D images without requiring explicit manual modeling, reducing device complexity while maintaining adaptability to dynamic scenes.
3Reliability
If multiple camera views are captured to create immersive surround views, then the user experience is improved, but the data size and storage requirements increase
Solution Approach 1:
The system merges multiple camera views into a unified multi-view representation by aligning and integrating data from different angles. Through view synthesis and rendering techniques, the system combines redundant information across views while preserving unique visual content, achieving immersive surround views with optimized data size that balances user experience quality and storage requirements.
Data Source
AI summary
Various embodiments of the present invention relate generally to systems and methods for collecting, analyzing, and manipulating images and video. According to particular embodiments, live images captured by a camera on a mobile device may be analyzed as the mobile device moves along a path. The live images may be compared with a target view. A visual indicator may be provided to guide the alteration of the positioning of the mobile device to more closely align with the target view.