Separated Camera Image Alignment Using 6DOF Pose Reprojection
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Solution Overview
Problem
Aligning image content from multiple cameras in mixed-reality systems, particularly when timestamp data is unavailable or when cameras operate in different time domains, poses challenges in compute efficiency and image alignment, especially for dual camera systems with a detached camera.
Innovation Solution
Generating a 3D feature map of the environment shared between an integrated and detached camera, determining 6 DOF poses for both cameras, and using this information to reproject and overlay images without requiring timestamp data, optionally performing parallax correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If timestamp data is used for image alignment, then image alignment precision is improved, but the system becomes unreliable when cameras operate in different time domains or when timestamp data is unavailable
Solution Approach 1:
The patent introduces 6DOF pose information as an intermediary element that mediates between multiple cameras operating in different time domains. Instead of directly comparing images using timestamps, the system uses pose information (position and orientation) of each camera relative to a common reference frame to align images. This intermediary approach allows reliable alignment even when timestamp data is unavailable or cameras operate independently.
Solution Approach 2:
The patent replaces the traditional timestamp-based temporal synchronization mechanism with a spatial-based 6DOF pose information system. By substituting the temporal alignment approach (using timestamps) with a spatial approach (using pose information including position and orientation), the system achieves reliable image alignment without being constrained by time domain differences between cameras.
2Measurement precision
If images from multiple separated cameras are aligned using traditional methods, then image alignment is achieved, but compute efficiency deteriorates due to the complexity of aligning images from cameras with different orientations and physical separations
Solution Approach 1:
The patent replaces complex computational image alignment algorithms with a more efficient pose-based reprojection method. Instead of performing computationally intensive feature matching and alignment calculations between images from separated cameras, the system uses pre-determined 6DOF pose information to directly reproject images to a common reference frame, significantly reducing computational complexity.
Solution Approach 2:
The patent performs preliminary determination of 6DOF pose information for each camera relative to a common reference frame before the actual image alignment process. This preliminary action allows the system to pre-compute transformation matrices and projection parameters, so that during runtime, image alignment becomes a simple reprojection operation rather than a complex iterative optimization problem.
3Adaptability or versatility
If a dual camera system with a detached camera is used, then image content alignment between the detached camera and both left and right cameras is achieved, but device complexity increases
Solution Approach 1:
The patent creates a universal alignment framework based on 6DOF pose information that can handle multiple camera configurations (integrated cameras, detached cameras, dual camera systems) through a single common reference frame approach. Instead of implementing separate alignment algorithms for each camera type or configuration, the system uses a universal pose-based reprojection method that works across all configurations, reducing overall system complexity.
Data Source
AI summary
Techniques for aligning images generated by an integrated camera physically mounted to an HMD with images generated by a detached camera physically unmounted from the HMD are disclosed. A 3D feature map is generated and shared with the detached camera. Both the integrated camera and the detached camera use the 3D feature map to relocalize themselves and to determine their respective 6 DOF poses. The HMD receives the detached camera's image of the environment and the 6 DOF pose of the detached camera. A depth map of the environment is accessed. An overlaid image is generated by reprojecting a perspective of the detached camera's image to align with a perspective of the integrated camera and by overlaying the reprojected detached camera's image onto the integrated camera's image.