3D Object Matching Using Depth Scanning for Part Identification
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Solution Overview
Problem
Traditional two-dimensional (2D) camera imaging technologies struggle to accurately identify and match objects due to lack of depth information, ambiguity in object size, and variability from external factors like lighting and viewpoint, making it difficult for non-experts to identify replacement parts in rapidly evolving product lines, such as plumbing fixtures.
Innovation Solution
The use of three-dimensional (3D) scanning technology for object identification, which involves creating a comprehensive database of 3D object scans, allowing users to scan objects with 3D scanners or mobile devices, and comparing the scans to the database using hierarchical, holistic, or feature-based methods to provide accurate and efficient matching results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D camera imaging is used for object identification, then the system is simple and easy to operate, but the identification accuracy is low due to lack of depth information and ambiguity in object size
Solution Approach 1:
The patent transitions from 2D camera imaging to 3D scanning technology, adding the depth dimension to object representation. This dimensional change enables accurate capture of true size and shape, resolving the identification accuracy problem while maintaining operational simplicity through automated 3D processing pipelines
2Reliability
If 2D imaging is used, then the device is simple, but environmental factors like lighting and viewpoint cause variability that reduces identification reliability
Solution Approach 1:
By capturing objects in 3D space, the system eliminates the viewpoint and lighting dependencies inherent in 2D imaging. The third dimension provides invariant geometric properties that remain consistent regardless of environmental conditions, significantly improving identification reliability
Solution Approach 2:
The patent creates digital 3D copies of physical objects that can be stored and compared in a database. These digital replicas preserve the true geometric properties of objects, allowing for reliable identification without being affected by environmental variations in the imaging process
3Adaptability or versatility
If a comprehensive database of parts is created to improve identification accuracy, then the database becomes massive and difficult to sift through, but coverage of rare and obsolete parts improves
Solution Approach 1:
The patent replaces manual database searching with automated 3D pattern recognition algorithms. The system uses computational methods to automatically compare scanned objects against the comprehensive database, eliminating the need for manual sifting through massive part catalogs while maintaining high identification accuracy for both common and rare parts
4Measurement precision
If expert knowledge is required to identify parts, then identification accuracy for rare parts improves, but the system becomes inaccessible to non-experts and requires years of training
Solution Approach 1:
The patent enables the system to perform expert-level identification automatically without requiring user expertise. The 3D scanning and automated matching system serves itself by using objective geometric measurements and algorithmic comparison, eliminating the need for human experts to manually identify parts while maintaining high accuracy
Solution Approach 2:
The system replaces human expert knowledge with automated 3D recognition algorithms. The computational system processes geometric data and performs pattern matching objectively, providing expert-level identification accuracy to any user regardless of their training or experience level
Data Source
AI summary
The system and method utilize three-dimensional (3D) scanning technology to create a database of profiles for good, product, object, or part information by producing object representations that permit rapid, highly-accurate object identification, matching, and obtaining information about the object, which is not afforded by traditional two-dimensional (2D) camera imaging. The profiles can compared to a profile of an unknown object to identify, match, or obtain information about the unknown object, and the profiles can be filtered to identify or match the profiles of known objects to identify and/or gather information about an unknown object.