Camera Array 3D Reconstruction for Real-Time Depth and Navigation
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Solution Overview
Problem
Existing 3D scene information generation methods, such as LiDAR and radar, suffer from limitations in angular resolution, interference, and inability to capture visual appearance, while binocular disparity methods face errors in textureless regions and occlusions, and camera arrays are constrained by micro-lens arrays, limiting depth and angular resolution.
Innovation Solution
A camera array system with multiple cameras capturing spectral data, synchronized to generate a voxel space by determining voxel occupancy probabilities, producing a 3D representation and 3D video stream through image processing and normalization, enabling accurate depth estimation and real-time 3D point cloud generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR is used to create 3D scene information, then depth measurement capability is improved, but angular resolution deteriorates at long range
Solution Approach 1:
The patent segments the imaging function into multiple cameras arranged in an array, each capturing a portion of the scene. This segmentation allows the system to achieve both good depth measurement (through multi-view geometry) and maintained angular resolution (through the collective coverage of multiple camera perspectives), resolving the contradiction inherent in single-point LiDAR measurement.
Solution Approach 2:
The patent transitions from 1D depth measurement (LiDAR range) to 2D image space analysis across multiple views. By using multiple cameras to capture 2D images from different positions and processing them through voxel-based 3D reconstruction, the system achieves accurate depth information without sacrificing angular resolution, as the dimensional transition provides redundant geometric constraints.
2Area of stationary object
If multiple LiDAR devices are deployed to improve coverage, then scene coverage is improved, but interference between laser pulses increases
Solution Approach 1:
The patent replaces the active mechanical LiDAR system (laser emission and detection) with a passive optical system using multiple cameras. This substitution eliminates laser pulse interference entirely while maintaining comprehensive scene coverage through the multi-camera array's collective field of view, allowing unlimited devices to operate simultaneously without interference.
3Measurement precision
If binocular disparity methods are used to generate 3D information, then depth estimation is improved, but accuracy deteriorates in textureless regions
Solution Approach 1:
The patent segments the depth estimation problem into multiple independent camera views beyond just a binocular pair. By incorporating observations from multiple additional cameras, the system provides redundant geometric constraints that enable more reliable depth estimation in textureless regions, where multiple viewing angles can disambiguate depth even when texture matching fails.
Solution Approach 2:
The patent extends the binocular disparity approach by transitioning from 2-view to multi-view geometry. This dimensional expansion from stereoscopy to polycopy provides additional geometric constraints that improve depth estimation reliability in textureless regions, as the voxel-based reconstruction can leverage epipolar geometry from multiple camera baselines rather than relying solely on texture correspondence.
4Volume of moving object
If micro-lens array camera arrays are used to increase camera density, then device compactness is improved, but depth and angular resolution accuracy deteriorates
Solution Approach 1:
The patent segments the imaging function across multiple spatially separated cameras rather than using densely packed micro-lenses. This segmentation approach allows each camera to maintain full sensor resolution and optimal optics, achieving high depth and angular resolution accuracy while the overall array provides the necessary multi-view coverage for 3D reconstruction.
Solution Approach 2:
The patent transitions from 2D sensor plane sampling (micro-lens array) to 3D spatial distribution of camera centers. By arranging cameras in a three-dimensional configuration with known baselines, the system achieves accurate depth and angular resolution through multi-view geometry, overcoming the fundamental limitation of micro-lens arrays where the baseline is constrained by the sensor chip dimensions.
Data Source
AI summary
The present disclosure is directed to systems and/or methods that may be used for determining scene information (for example, 3D scene information) using data obtained at least in part from a camera array. Certain embodiments may be used to create scene measurements of depth (and the probability of accuracy of that depth) using an array of cameras. One purpose of certain embodiments may be to determine the depths of elements of a scene, where the scene is observed from a camera array that may be moving through the scene. Certain embodiments may be used to determine open navigable space and to calculate the trajectories of objects that may be occupying portions of that space. In certain embodiments, the scene information may be used to generate a virtual space of voxels where the method then determines the occupancy of the voxel space by comparing a variety of measurements, including spectral response.