Four-Camera Array 3D Measurement Algorithm Simplification
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Solution Overview
Problem
Current three-dimensional measurement technologies face challenges in quickly and accurately calculating the external dimensions of objects, particularly with dynamic objects and in achieving precise three-dimensional stereoscopic measurements, as existing methods like single-point vision and planar vision struggle with morphology grasping and edge feature extraction, and binocular or multi-view vision measurements are complex and not widely used due to difficulties in edge feature extraction and pixel matching.
Innovation Solution
A feature-point three-dimensional measuring system using a planar array of four digital cameras arranged in a rectangular formation, where cameras are identical and have parallel optical axes, simplifying the matching algorithm by reducing the epipolar constraint to straight lines parallel to the X and Y axes, allowing for direct translation and comparison of pixel points to calculate spatial coordinates of feature points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binocular or multi-view vision measuring technology is used, then three-dimensional measurement capability is achieved, but device complexity and algorithm difficulty increase
Solution Approach 1:
The patent divides the camera system into multiple independent camera units arranged in a planar array, where each camera captures images from its own viewpoint. This segmentation allows the complex three-dimensional measurement problem to be broken down into simpler individual camera observations that can be processed independently and then combined, reducing overall system complexity while maintaining measurement capability.
Solution Approach 2:
The patent transitions from traditional binocular vision (two cameras) to a planar array of multiple cameras, adding spatial dimensions to the measurement system. By arranging cameras in a two-dimensional plane and capturing images from multiple viewpoints simultaneously, the system achieves enhanced three-dimensional measurement capability while maintaining manageable complexity through structured geometry.
2Measurement precision
If binocular or multi-view vision measuring technology is used, then three-dimensional measurement capability is achieved, but algorithm complexity increases
Solution Approach 1:
The patent applies different processing strategies to different regions and features in the images. By identifying and processing local feature points (such as corners, edges, and distinctive markers) rather than attempting to match all pixels, the system simplifies the algorithm while maintaining accuracy. Each local feature is processed independently with appropriate matching techniques, reducing overall computational complexity.
Solution Approach 2:
The patent uses virtual camera models and simulated image data to simplify the matching process. By creating a virtual representation of the camera array and its geometric relationships, the system can pre-calculate projection matrices and transformation parameters, reducing the complexity of real-time pixel matching algorithms while maintaining measurement precision.
3Productivity
If single-point vision measuring method is used, then measurement speed is improved, but morphology characteristics cannot be quickly and fully grasped
Solution Approach 1:
The patent merges the advantages of single-point vision (fast measurement) with multi-point vision (comprehensive morphology capture) by combining multiple cameras into a planar array that simultaneously captures images from multiple viewpoints. This merged system processes images from all cameras in parallel, achieving both high measurement speed and complete morphology characterization of the three-dimensional object.
Solution Approach 2:
The patent implements continuous capture and processing of images from all cameras in the array simultaneously, maintaining uninterrupted measurement operation. By continuously acquiring data from multiple viewpoints and processing it in real-time, the system maintains high measurement speed while building complete three-dimensional morphology information without interruption or loss of data.
4Ease of manufacture
If planar vision measuring method is used, then two-dimensional imaging is achieved, but three-dimensional physical dimensions cannot be directly calculated
Solution Approach 1:
The patent introduces a virtual three-dimensional coordinate system and projection geometry as an intermediary between the two-dimensional camera images and the actual three-dimensional object dimensions. By establishing mathematical relationships between image coordinates and spatial coordinates through calibration and geometric modeling, the system enables direct calculation of three-dimensional physical dimensions from the two-dimensional images while maintaining ease of implementation.
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 method enables quick and accurate calculation of three-dimensional stereoscopic coordinates, simplifying the matching algorithm and improving measurement accuracy without the need for complex calibration, allowing for precise three-dimensional point cloud data generation and reproduction.
Implementation Method 1
four digital cameras (camera a, camera b, camera c, and camera d) are arranged on the same plane... each camera having a focal point on its imaging optical axis... after the four cameras image the same measured object at the same time
Data Source
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AI summary
A feature-point three-dimensional measuring system of a planar array of a four-camera group and measuring method, in the technical field of products and methods of optical electronic measuring technologies, comprising establishing a measuring system of at least one four-camera group wherein four digital cameras form a 2×2 array group; matching an image feature point acquired by the camera group; upon matched feature point image coordinates, calculating coordinates of spatial locations of respective feature points; upon coordinates of the spatial locations, calculating other three-dimensional dimensions of the measured object to be specially measured to form three-dimensional point cloud data and establish a three-dimensional point cloud graph for performing three-dimensional stereoscopic reproduction. Here, full matching is performed for all measured points of the measured object by directly translating, superimposing, and comparing point by point the pixel points of measured images in X and Y-axes directions. It greatly simplifies the complex algorithm of binocular matching, achieving effect of simply, quickly, accurately, and directly measuring three-dimensional dimensions of an object with a multi-view camera.