Computer-Vision Image Capture Guidance for 3D Feature Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer-vision techniques for generating 3D models from 2D images are limited by low resolution photos, blur, occlusion, and lack of feature correspondences, which reduce the effectiveness of image analysis and 3D reconstruction.
Innovation Solution
A computer-vision network is employed to enhance image capture by providing viewfinder guidance and feedback to maximize feature correspondences, using intra-image and inter-image parameter evaluation systems to improve framing and image quality for 3D model reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer-vision techniques are used to generate 3D models from 2D images, then 3D model reconstruction is enabled, but the quality is limited by low resolution photos, blur, occlusion, and lack of feature correspondences
Solution Approach 1:
The system performs preliminary evaluation of image parameters (resolution, blur, occlusion, feature correspondences) before 3D model reconstruction to identify and flag images that do not meet quality thresholds, allowing for pre-processing or re-capture of deficient images
Solution Approach 2:
The system provides feedback on image quality metrics and feature correspondence levels to guide users in capturing additional or improved images, iteratively enhancing the input data quality for 3D reconstruction
2Quantity of substance
If multiple images are captured from different viewpoints to improve 3D model completeness, then coverage increases, but the complexity of image capture and processing increases
Solution Approach 1:
The system automatically evaluates image sets for completeness and identifies specific gaps in coverage, providing self-guided instructions to users on what additional images are needed without requiring manual planning or complex capture protocols
3Manufacturing precision
If border pixels are evaluated to provide framing guidance, then image capture quality improves, but processing time and computational load increase
Solution Approach 1:
The system extracts and evaluates only border pixels from images rather than processing all pixels, significantly reducing computational load while still providing effective framing guidance for image capture
Data Source
AI summary
Disclosed are techniques for enhancing two-dimensional (2D) image capture of subjects (e.g., a physical structure, such as a residential building) to maximize the feature correspondences available for three-dimensional (3D) model reconstruction. More specifically, disclosed is a computer-vision network configured to provide viewfinder interfaces and analyses to guide the improved capture of an intended subject for specified purposes. Additionally, the computer-vision network can be configured to generate a metric representing a quality of feature correspondences between images of a complete set of images used for reconstructing a 3D model of a physical structure. The computer-vision network can also be configured to generate feedback at or before image capture time to guide improvements to the quality of feature correspondences between a pair of images.