Camera Path Extraction Engine for Augmented Reality Video
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Solution Overview
Problem
Current augmented reality systems, such as those using optical head-mounted displays, face challenges in accurately extracting camera paths and 3D objects from 2D video feeds, which limits their ability to seamlessly integrate and manipulate 3D objects within real-world environments.
Innovation Solution
A video processing system comprising a camera path extraction engine, a 3D object extraction engine, and an integration engine that analyzes 2D video frames to estimate camera trajectories, extract 3D objects, and integrate these objects into the video, using techniques like feature tracking, optical flow, and depth mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 2D video processing methods are used, then system simplicity is maintained, but accuracy in extracting camera paths and 3D objects deteriorates
Solution Approach 1:
The system divides the complex task of AR integration into separate functional modules: a camera path extraction engine that processes video frames to determine camera movement, a 3D object extraction engine that identifies objects and generates depth maps, and an integration engine that combines these results. This segmentation allows each module to specialize in specific processing tasks, improving overall accuracy while managing system complexity through modular design.
Solution Approach 2:
The system transforms 2D video data into 3D spatial information by generating depth maps and estimating camera trajectories in three-dimensional space. The 3D object extraction engine creates depth representations from 2D video frames, and the camera path extraction engine reconstructs 3D camera movement from 2D frame sequences, enabling accurate AR object placement in virtual 3D space based on 2D video input.
2Measurement precision
If feature tracking and optical flow techniques are implemented, then camera path extraction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by extracting key features and computing optical flow between consecutive video frames to establish correspondence points before full 3D reconstruction. The camera path extraction engine uses these pre-computed feature correspondences to efficiently estimate camera trajectories, reducing the computational burden of subsequent processing steps while maintaining accuracy.
Solution Approach 2:
The system uses the video frames themselves to provide the information needed for processing - feature points are automatically detected and tracked within the video data, and optical flow is computed from the temporal variations in the video sequence. This self-service approach eliminates the need for external calibration data or manual feature marking, improving accuracy while keeping processing efficient by utilizing only the available video information.
3Reliability
If 3D objects are extracted and integrated into 2D video, then augmented reality quality is enhanced, but computational complexity increases
Solution Approach 1:
The system merges the 2D video feed with extracted 3D object data and camera path information to create an integrated augmented reality output. The integration engine combines the depth maps from 3D object extraction with the camera trajectories from path extraction, allowing virtual objects to be correctly positioned and scaled in the augmented reality scene while maintaining compatibility with the original 2D video input.
Solution Approach 2:
The system introduces intermediate representations - depth maps generated by the 3D object extraction engine and camera path data generated by the camera path extraction engine - that serve as mediators between the raw 2D video input and the final augmented reality output. These intermediates simplify the integration process by providing structured spatial and temporal information that makes combining multiple data sources more manageable and computationally efficient.
Data Source
AI summary
A system includes one or more hardware processors, a head mounted display (HMD), a digital camera device, and a path extraction engine. The path extraction engine is configured to receive two-dimensional (2D) video from the digital camera device, the 2D video presenting a 2D view of a three-dimensional (3D) object, the digital camera device moving along a camera path as the digital camera device captures the 2D video, identify a plurality of 2D features associated with the 3D object within the 2D video, track the plurality of 2D features across multiple frames of the 2D video, estimate motion in pixels associated with each 2D feature of the plurality of 2D features, using the estimated motion in the pixels, identify 2D motion caused by the digital camera device moving along the camera path, and compute a first camera position of the digital camera device based on the identified 2D motion.