Composite Image Generation for Multi-Sided Object Capture
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Solution Overview
Problem
Existing machine vision systems struggle to efficiently capture and analyze images of objects that are larger than the field of view or are moving relative to the imaging device, particularly in capturing multiple sides of an object effectively.
Innovation Solution
A method and system for generating images of multiple sides of an object by receiving information about the 3D pose of the object, capturing images using multiple image sensors, mapping 3D object surfaces to 2D image areas, associating image portions with object surfaces, and generating composite images of the object surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a single image sensor is used to capture an object, then the device complexity is low, but the image coverage and resolution are insufficient for objects larger than the field of view
Solution Approach 1:
The system divides the imaging task into multiple segments by using multiple image sensors, each capturing a specific portion of the object. This allows the entire object to be covered without requiring a single complex sensor with excessively wide field of view, thus resolving the contradiction between coverage area and device complexity.
Solution Approach 2:
The system transitions from 2D image capture to 3D spatial understanding by incorporating depth information from the dimensioning system. This enables intelligent selection and stitching of images from multiple sensors based on the object's three-dimensional pose, achieving comprehensive coverage while managing sensor complexity through spatial optimization.
2Area of stationary object
If multiple images are captured to cover the entire object, then the image coverage is improved, but the processing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by capturing the object's 3D pose information using the dimensioning system before image processing. This pre-acquired spatial information is used to guide the selection and stitching of images, significantly reducing the computational time required compared to processing all captured images without such guidance.
Solution Approach 2:
The system implements a feedback mechanism where the 3D pose information from the dimensioning system continuously guides the image capture and processing workflow. This real-time feedback enables intelligent selection of relevant images and optimizes the stitching process, reducing overall processing time while maintaining comprehensive coverage.
3Loss of information
If images are captured from multiple angles to show all sides of an object, then the completeness of object representation is improved, but the system complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating a dimensioning system that serves dual purposes: it provides depth information for 3D pose determination and simultaneously guides the image capture process. This universal component reduces the need for separate complex coordination systems while ensuring complete object surface information is captured.
4Measurement precision
If high-resolution images are captured of the entire object, then the image resolution is improved, but the field of view requirement becomes unrealistic for large objects
Solution Approach 1:
The system segments the high-resolution imaging task across multiple sensors, each capturing a detailed portion of the object within their respective field of view. This allows maintaining high resolution for each captured region while collectively covering the entire large object, resolving the contradiction between resolution and field of view requirements.
Data Source
AI summary
In accordance with some embodiments of the disclosed subject matter, methods, systems, and media for generating images of multiple sides of an object are provided. In some embodiments, a method comprises receiving information indicative of a 3D pose of a first object in a first coordinate space at a first time; receiving a group of images captured using at least one image sensor, each image associated with a field of view within the first coordinate space; mapping at least a portion of a surface of the first object to a 2D area with respect to the image based on the 3D pose of the first object; associating, for images including the surface, a portion of that image with the surface of the first object based on the 2D area; and generating a composite image of the surface using images associated with the surface.