Multi-Camera 3D Reconstruction Without Fixed Camera Poses
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Solution Overview
Problem
Existing three-dimensional content creation techniques rely on apriori knowledge of camera pose and relative pose, limiting their applicability to static scenes and requiring fixed camera positions, which hinders the generation of dynamic and complex 3D reconstructions.
Innovation Solution
Techniques for synchronizing data from multiple independently operated cameras to determine relative poses without prior knowledge, using feature tracking and inertial sensor data to generate three-dimensional reconstructions of static and moving scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single camera with known pose information is used to create three-dimensional content, then the system is simple and easy to operate, but it is limited to static objects and scenes
Solution Approach 1:
The patent transitions from static camera pose assumptions to dynamic pose estimation. Multiple cameras are allowed to move freely while their poses are continuously estimated through feature tracking and optimization algorithms, enabling the system to handle both static and dynamic scenes without requiring fixed camera positions.
Solution Approach 2:
The patent introduces feature points and optimization algorithms as intermediaries between the cameras and the 3D reconstruction process. These features serve as mediators that link multiple camera views together, enabling pose estimation and 3D reconstruction even when camera poses are unknown and cameras are in motion.
2Adaptability or versatility
If two or more cameras are employed with fixed and predetermined geometric constraints, then motion in the scene can be captured, but the camera positions and orientations must be prescribed and fixed
Solution Approach 1:
The patent eliminates fixed geometric constraints between cameras by implementing dynamic pose estimation. Each camera's pose is independently estimated through feature tracking across multiple frames, allowing cameras to move freely in space while maintaining the ability to reconstruct 3D content from their combined views.
Solution Approach 2:
The system performs self-calibration through the optimization process. Rather than requiring predetermined geometric constraints, the cameras' relative poses are automatically determined through the optimization algorithm that minimizes reprojection errors of tracked features, allowing the system to self-determine its geometric configuration.
3Ease of manufacture
If traditional techniques relying on apriori knowledge of camera pose are used, then the processing is simpler, but the applicability is limited to static scenes and fixed camera positions
Solution Approach 1:
The patent performs preliminary feature tracking and pose estimation before final 3D reconstruction. By pre-processing the image sequences to estimate camera poses and track features through the sequence, the system prepares the necessary geometric information in advance, enabling subsequent 3D reconstruction to proceed efficiently even with complex camera motions.
Solution Approach 2:
The optimization process uses feedback from reprojection errors to iteratively refine pose estimates. The system continuously compares projected 3D feature positions with actual observed positions in images, using the error feedback to adjust and improve pose estimates, thereby achieving accurate 3D reconstruction without requiring prior pose knowledge.
Data Source
AI summary
Techniques for generating three-dimensional content from the recordings of one or more independently operated cameras that are not constrained to fixed positions and orientations are disclosed. In some embodiments, data associated with a camera recording a scene is received, wherein camera pose with respect to the scene is not known; camera pose with respect to the scene is determined based on received image data captured by the camera and received sensor data associated with the camera; and the received data and determined camera pose are used to facilitate generation of at least a portion of a point cloud corresponding to the scene.