3D Modeling System Using Segmented Sensing Components
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Solution Overview
Problem
Current 3D modeling techniques face challenges in accurately generating three-dimensional views of objects by associating characteristics with respective data points, particularly in efficiently collecting and processing data from multiple sensing components to render detailed and accurate 3D images.
Innovation Solution
A system comprising multiple sensing components and computing devices that determine and associate object characteristics with data points based on rendering parameters, utilizing a server with modules for model building, texture generation, semantics indexing, shader application, and material application to generate a 3D view of the object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensing components are used to collect detailed object characteristics, then the quality and detail of 3D modeling is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system divides the sensing components into multiple independent units (depth sensing component, color sensing component, normal mapping sensing component) that can be configured in different combinations. Each sensing component captures specific characteristics (depth, color, surface normals) that are processed separately and then integrated to form the complete 3D model, allowing modular complexity management while maintaining high measurement precision.
Solution Approach 2:
The server is designed with universal functionality to handle data from various types of sensing components. The same server infrastructure can process data from different sensing component configurations (single component, dual component, or triple component setups), making the system adaptable to different complexity levels while maintaining consistent 3D modeling quality through standardized processing pipelines.
2Manufacturing precision
If multiple sensing components capture comprehensive object data, then the rendering quality and material representation are enhanced, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of sensing component data by associating captured characteristics with respective data points on the object before full 3D model generation. The server pre-processes depth maps, color images, and normal maps into standardized formats and associates them with corresponding spatial locations, reducing the computational burden during final rendering and material application stages.
Solution Approach 2:
The system allows selective use of sensing components based on required rendering quality. Users can configure the system to use only essential components (e.g., depth and color) for basic 3D modeling, or add optional components (e.g., normal mapping) for enhanced rendering quality. This partial action approach enables balancing processing time against rendering quality based on specific application needs.
3Measurement precision
If detailed object characteristics are associated with respective data points, then the 3D view accuracy is improved, but the computational complexity for data association increases
Solution Approach 1:
The system associates object characteristics with specific local data points rather than applying global transformations. Each data point on the 3D model receives localized information from sensing components (depth at specific coordinates, color at specific pixels, normal vectors at specific surface points), ensuring high association accuracy while using simple coordinate-based matching algorithms that minimize computational complexity.
Data Source
AI summary
Systems and methods for collecting data from an object are provided. In examples, a plurality of sensing components are configured to receive information indicative of one or more characteristics of the object. The information indicative of one or more characteristics of the object can be associated with respective data points of the object. The system is further configured to generate a three-dimensional (3D) view of the object based on the information indicative of one or more characteristics of the object and the association with respective data points.