3D Cylindrical Object Dimensioning Under Optical Occlusion
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Solution Overview
Problem
Conventional techniques fail to accurately dimension cylindrical objects due to optical occlusion in 3D image data, leading to inefficiencies and inaccuracies, particularly when using mobile devices in motion or lacking position and orientation data.
Innovation Solution
A system and method that automatically detects cylindrical objects, filters 3D image data to compensate for optical occlusion, segments the data into horizontal sections, determines the radius of each section, and generates a 3D model for precise dimensioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image data techniques are used to dimension cylindrical objects, then the dimensioning process is simple and fast, but the measurement precision deteriorates due to optical occlusion causing the object to appear smaller than its actual size
Solution Approach 1:
The patent segments the cylindrical object into multiple horizontal sections and processes each section independently to determine local radii, thereby overcoming the optical occlusion effect that causes the entire object to appear smaller. This segmentation approach allows accurate measurement of each cross-section while maintaining computational feasibility.
Solution Approach 2:
The patent transitions from 2D image data to 3D depth data to compensate for optical occlusion. By utilizing depth information from time-of-flight sensors or stereo vision, the system reconstructs the true 3D geometry of the cylindrical object, eliminating the apparent size reduction caused by perspective projection.
2Productivity
If manual measurement techniques are used, then measurement precision can be maintained, but productivity deteriorates due to time-consuming manual intervention and increased labor costs
Solution Approach 1:
The system performs automatic dimensioning of cylindrical objects using image processing algorithms that detect edges, fit circles to cross-sections, and calculate radii without human intervention. The automated pipeline includes preprocessing, edge detection, circular Hough transform or least-squares fitting, and dimension extraction, eliminating the need for manual measurement while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical measurement tools (rulers, calipers, tape measures) with optical and computational systems. Image sensors capture the object, and computer vision algorithms automatically extract dimensional information, substituting human-operated mechanical systems with automated optical-mechanical-computational systems.
3Measurement precision
If 3D depth data is used to compensate for optical occlusion, then measurement precision improves, but use of energy and processing time worsen due to the complexity of filtering and segmenting the data
Solution Approach 1:
The patent applies preprocessing filters to the 3D depth data before segmentation to remove noise and artifacts early in the processing pipeline. By cleaning the data beforehand, subsequent segmentation and radius calculation steps operate on higher-quality input, improving accuracy while reducing the need for iterative refinement and reprocessing.
Data Source
AI summary
Devices and methods for dimensioning an object are disclosed herein. The method receives, from at least one sensor, at least one image of an object. The at least one image is indicative of a first perspective of the object and includes three-dimensional (3D) image data of the object. The method detects whether the object is cylindrical. Responsive to detecting the object is cylindrical, the method compensates for optical occlusion present in the 3D image data by filtering the 3D image data; segmenting the filtered 3D image data into horizontal sections; determining a radius of an arc of each horizontal section; generating a 3D model of the object based on the determined radii; and dimensioning the object based on the generated 3D model.