Photography-Based 3D Modeling Using Standard Camera and Deep Learning
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Solution Overview
Problem
Conventional photography-based 3D modeling methods are either costly due to complex hardware requirements or computation-intensive, making them unsuitable for wide adoption and rapid modeling on limited devices, and often require manual intervention for accurate results.
Innovation Solution
A photography-based 3D modeling system utilizing deep learning and image processing to generate 3D models from a single photo capture point on mobile devices or cloud servers, supporting various photo capture devices, and automatically assembling models based on position and direction information to create overall 3D models and 2D floorplans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional photography-based 3D modeling methods using depth recording cameras are used, then 3D modeling accuracy is improved, but hardware complexity and equipment cost increase
Solution Approach 1:
The patent replaces complex depth recording hardware with a standard camera combined with computational methods. Instead of using specialized cameras with depth sensors, the system uses ordinary photography combined with feature point matching and coordinate transformation algorithms to achieve 3D modeling, thereby substituting mechanical/optical complexity with computational processing.
Solution Approach 2:
The patent creates a virtual 3D model as a copy of the physical space using only 2D photographs. By capturing spatial information through standard camera images and reconstructing the 3D environment computationally, the system produces an accurate digital replica without requiring specialized depth-capturing hardware.
2Extent of automation
If Multi View Stereo (MVS) modeling is used with multiple photo capture points, then automation is improved, but computing resource requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for 3D modeling from photographs, specifically feature points and their coordinates. Instead of processing entire images or using computationally intensive MVS algorithms, the system extracts key spatial features and uses coordinate transformation based on camera position and orientation data, significantly reducing computing resource requirements while maintaining automation.
3Manufacturing precision
If photo capture points are positioned densely for accurate modeling, then modeling precision is improved, but operation convenience and time consumption worsen
Solution Approach 1:
The patent incorporates feedback mechanisms where the system provides real-time guidance to users during the photo capture process. By using the captured photographs along with camera position and orientation information to progressively build and refine the 3D model, the system can guide users on where to position the next photo capture point, balancing modeling precision with operational convenience without requiring excessively dense point distribution.
4Manufacturing precision
If manual intervention is used for 3D model correction and assembly, then modeling precision is improved, but time consumption and labor intensity increase
Solution Approach 1:
The patent implements self-service automation where the system automatically performs feature point matching, coordinate transformation, and 3D model assembly without requiring manual intervention. The system uses the captured photographs along with automatically obtained camera position and orientation information to construct and refine the 3D model autonomously, eliminating time-consuming manual correction and assembly processes while maintaining modeling precision.
Data Source
AI summary
The present disclosure discloses a photography-based 3D modeling system and method, and an automatic 3D modeling apparatus and method, including: (S1) attaching a mobile device and a camera to the same camera stand; (S2) obtaining multiple images used for positioning from the camera or the mobile device during movement of the stand, and obtaining a position and a direction of each photo capture point, to build a tracking map that uses a global coordinate system; (S3) generating 3D models on the mobile device or a remote server based on an image used for 3D modeling at each photo capture point; and (S4) placing the individual 3D models of all photo capture points in the global three-dimensional coordinate system based on the position and the direction obtained in S2, and connecting the individual 3D models of multiple photo capture points to generate an overall 3D model that includes multiple photo capture points.