Hybrid Camera Array Self-Calibration With Depth-Based Scale Recovery
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Solution Overview
Problem
Existing methods for calibrating hybrid camera arrays, which combine color and depth sensors, face challenges in accurately determining extrinsic parameters between multiple devices due to the lack of precise feature matching across different modalities and the ambiguity of scale factors in depth maps, leading to inaccurate 3D reconstruction.
Innovation Solution
A method involving structure from motion algorithms to generate up to scale camera poses and point clouds, followed by estimating scaling using depth maps to calibrate camera poses accurately, utilizing error metrics based on depth and pixel-based comparisons to minimize errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structure from motion algorithms are used for self-calibration, then calibration accuracy is improved, but the scale factor remains ambiguous and unknown
Solution Approach 1:
The patent introduces depth maps as an intermediary element that bridges the gap between structure from motion calibration and metric scale recovery. The depth maps provide metric depth information that serves as a reference to resolve the ambiguous scale factor, allowing the system to maintain both high calibration accuracy and recover absolute scale without requiring calibration patterns.
2Ease of manufacture
If depth maps are used for geometry-based calibration, then calibration is achieved without calibration patterns, but precision deteriorates due to noise and lack of spatial detail
Solution Approach 1:
The patent merges two complementary approaches: structure from motion algorithms that provide accurate geometric calibration up to scale, and depth maps that provide metric depth information. By combining these two sources of information, the system achieves both the simplicity of pattern-free calibration and the precision needed for accurate 3D reconstruction, overcoming the limitations of using either approach alone.
3Adaptability or versatility
If feature matching across different modalities is attempted, then multi-modal calibration is achieved, but matching accuracy deteriorates
Solution Approach 1:
The patent segments the calibration process into two distinct stages: first, structure from motion algorithms perform calibration using only color images where feature matching is reliable; second, depth maps are used separately to recover metric scale information. This segmentation avoids the problem of inaccurate cross-modal feature matching while still achieving versatile multi-modal calibration capability.
Data Source
AI summary
A method for calibrating a camera pose in a hybrid camera array comprising two or more color sensors and one or more depth sensors. The method comprises obtaining a depth map for each of the depth sensors, obtaining a set of images from the color sensors and generating up to scale camera poses for the color sensors and an up to scale point cloud using the set of images. A scaling of the up to scale camera poses and up to scale point cloud is then estimated using the one or more depth maps.