3D Virtual Object Search Using Geometry-Based Constraint Matching
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Solution Overview
Problem
Current 3D search systems lack the ability to accurately capture the complex and nuanced aspects of 3D objects, leading to limited and irrelevant search results due to reliance on keyword-based queries, manual categorization, and inconsistent metadata, and fail to incorporate user-defined constraints or descriptors.
Innovation Solution
A unified content-based 3D geometry search system that identifies constraints and descriptors from user queries, performs searches based on actual 3D object properties, and calculates fitness scores to rank search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword-based queries are used to search 3D objects, then the search process is simple and fast, but the search accuracy and relevance are limited due to inability to capture complex object characteristics
Solution Approach 1:
The patent combines keyword-based metadata search with 3D geometry-based search into a unified hybrid search system. The system processes both text queries and 3D model comparisons simultaneously, merging the speed advantage of keyword search with the accuracy advantage of geometry-based matching to resolve the contradiction between search speed and accuracy.
Solution Approach 2:
The search system is designed to handle multiple types of queries universally - both text-based keyword queries and 3D model-based queries. It can process different query types through a single unified framework that adapts to the input type, enabling the system to maintain simplicity for keyword searches while providing high accuracy for complex 3D object matching.
2Reliability
If manual categorization is used to organize 3D objects, then objects can be organized into structured categories, but the process is time-consuming and subjective
Solution Approach 1:
The patent replaces the manual mechanical categorization process with automated 3D geometry-based matching and machine learning algorithms. The system automatically compares 3D object geometries, extracts features, and performs classification without human intervention, eliminating the time-consuming and subjective nature of manual categorization while maintaining or improving consistency through algorithmic objectivity.
Solution Approach 2:
The system enables 3D objects to be automatically categorized and organized through self-service mechanisms where the 3D models themselves provide their geometric data for comparison and classification. The objects are automatically tagged and categorized based on their intrinsic geometric properties without requiring external manual labeling, reducing time investment while ensuring consistent categorization.
3Ease of operation
If metadata is used to describe 3D objects, then search can be performed using text descriptors, but the metadata quality and consistency are insufficient to capture full object features
Solution Approach 1:
The patent applies local quality by using different representation methods for different aspects of 3D objects. Metadata is used for high-level semantic descriptors (maintaining ease of operation), while detailed geometric features, surface properties, and structural characteristics are captured through 3D model analysis (preventing information loss). This multi-level approach ensures both convenience and completeness.
Solution Approach 2:
The system creates a composite representation of 3D objects that combines multiple data types: text metadata, geometric features, surface properties, and structural characteristics. This composite approach integrates the advantages of text-based search with the richness of 3D model data, ensuring both ease of operation and complete feature capture without relying on insufficient metadata alone.
4Measurement precision
If user-defined constraints are incorporated into 3D search, then search results can be more precisely tailored to user needs, but the search system complexity increases
Solution Approach 1:
The patent segments the search system into distinct modular components: a query parsing module that handles user constraints, a 3D model processing module that extracts geometric features, a matching engine that compares constraints with model properties, and a ranking module that orders results. This segmentation allows the system to incorporate complex user-defined constraints while managing overall system complexity through modular design, where each component handles specific tasks independently.
Data Source
AI summary
Some implementations relate to methods, systems, and computer-readable media to search for and retrieve three-dimensional (3D) objects based on user queries. An exemplary method comprises receiving a first query for one or more 3D objects. At least one constraint is identified from the first query, wherein the at least one constraint includes a particular descriptor that specifies one or more aspects of matching 3D objects. A second query is generated comprising a set of descriptors comprising the particular descriptor. A search is then performed, using the second query, to search a data repository containing 3D content, to obtain one or more 3D object search results having features responsive to the first query. A response that includes one or more 3D object search results may then be outputted as a response to the first query.