Mixed-Modality Stereo Camera Arrays for Depth Sensing
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Solution Overview
Problem
Existing stereo camera systems face challenges in accurately detecting correspondences between images captured using viewpoint-dependent imaging modalities, such as polarization, due to changes in light intensity and texture, which can lead to high error rates and noisy depth maps, especially when trying to align images from different viewpoints.
Innovation Solution
A mixed-modality stereo camera array system is employed, comprising multiple camera arrays with cameras configured to capture images in various modalities like non-polarized and polarized visible, infrared, thermal, and ultraviolet, along with radar and lidar, using linear polarization filters at different angles, and a processing circuit to fuse and synchronize images across arrays, enabling the detection of correspondences by mapping them to a common coordinate space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If viewpoint-dependent imaging modalities (e.g., polarization) are used to capture images, then additional information about surface properties and material characteristics is obtained, but light intensity and texture changes lead to high error rates in correspondence detection
Solution Approach 1:
The patent uses viewpoint-independent imaging modalities (such as intensity images or images from different spectral bands) as intermediary data to establish correspondences between viewpoint-dependent images. The processing circuit first identifies correspondences in the viewpoint-independent modality, then uses these correspondences to guide and improve correspondence detection in the viewpoint-dependent modalities, thereby mediating the information transfer while avoiding direct comparison errors.
Solution Approach 2:
The patent segments the correspondence detection process into multiple stages: first detecting correspondences in viewpoint-independent modalities, then using those results to inform correspondence detection in viewpoint-dependent modalities. This segmentation allows each stage to operate under optimal conditions, with the first stage providing a reliable foundation for the second stage.
2Adaptability or versatility
If multiple camera arrays with different imaging modalities are used, then comprehensive scene information is captured, but system complexity increases
Solution Approach 1:
The patent merges multiple camera arrays with different imaging modalities into a unified stereo camera system. The processing circuit integrates data from all modalities (polarization, intensity, spectral bands) and coordinates their operation to function as a single system for depth mapping and 3D reconstruction, thereby managing complexity through unified processing.
Solution Approach 2:
The patent designs the camera array system with universal processing capabilities that can handle multiple imaging modalities through a common processing pipeline. The processing circuit is configured to universally process different modalities using the same correspondence detection algorithms, allowing each camera array to serve multiple functions across different imaging modalities.
3Measurement precision
If images from different viewpoints are aligned using traditional stereo algorithms, then depth information can be computed, but errors and noise increase in textured or high dynamic range scenes
Solution Approach 1:
The patent changes the parameter space by introducing viewpoint-independent imaging modalities as an additional dimension for correspondence detection. Instead of relying solely on intensity or color information that varies with viewpoint, the system uses modalities where the same physical quantity appears consistently across different viewpoints, thereby improving reliability of depth measurements.
Solution Approach 2:
The processing circuit uses correspondence detection results from viewpoint-independent modalities as feedback to guide and refine correspondence detection in viewpoint-dependent modalities. This feedback mechanism allows the system to iteratively improve depth map quality by using reliable correspondences to correct or validate correspondences from less reliable modalities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of 3D reconstruction and depth sensing by leveraging viewpoint-independent imaging modalities to reliably identify correspondences in viewpoint-dependent modalities, reducing noise and improving the quality of depth maps, even in scenes with high dynamic range and lacking texture.
Implementation Method 1
The second camera of the first camera array may include a linear polarization filter at a first angle of linear polarization
Implementation Method 2
The first modality may be: non-polarized visible; non-polarized near-infrared; non-polarized thermal; non-polarized ultraviolet
Data Source
AI summary
A stereo camera array system includes: a first camera array at a first viewpoint including: a first camera configured to capture images in a first modality, the first modality being viewpoint-independent; and a second camera configured to capture images in a second modality different from the first modality; and a second camera array at a second viewpoint spaced apart along a first baseline from the first camera array at the first viewpoint, the second camera array including: a first camera configured to capture images in the first modality; and a second camera configured to capture images in the second modality.