Computer-Vision Capture Feedback for 3D Reconstruction
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, subjects out of frame, and lack of feature correspondences, which reduce the effectiveness of image analysis and 3D reconstruction.
Innovation Solution
A computer-vision network that provides viewfinder interfaces and feedback to guide improved image capture, maximizing feature correspondences by detecting and correcting framing issues and ensuring optimal angles for 2D images, and evaluating image pairs for sufficient feature matches to enhance 3D model reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computer-vision techniques are used to generate 3D models from 2D images, then 3D reconstruction capability is improved, but image quality limitations (low resolution, blur, occlusion, subjects out of frame) reduce the effectiveness of the analysis
Solution Approach 1:
The system performs preliminary evaluation of image quality metrics (resolution, blur, occlusion, framing) before 3D reconstruction to identify and flag images that do not meet quality thresholds, allowing for pre-processing or re-capture of substandard images
Solution Approach 2:
The system provides feedback on image quality assessments to users, indicating which images meet the required quality standards for 3D reconstruction and which need improvement, enabling iterative refinement of image capture
2Measurement precision
If more images are captured to improve feature correspondences, then 3D reconstruction accuracy is improved, but the time and complexity of the capture process increases
Solution Approach 1:
The system evaluates image pairs to determine the minimum sufficient number of images needed for adequate feature correspondences, capturing only the necessary quantity rather than requiring exhaustive image sets, thus achieving sufficient 3D reconstruction accuracy with reduced time investment
3Manufacturing precision
If stricter quality criteria are applied to image capture, then feature correspondence quality is improved, but the difficulty of capturing suitable images increases
Solution Approach 1:
The system provides real-time feedback on framing quality and feature correspondence potential, guiding users on how to adjust their capture approach to meet quality criteria without requiring extensive trial and error
Solution Approach 2:
The system performs preliminary assessment of potential feature correspondences in captured images before final processing, allowing for quick identification of usable images and reducing the need for extensive re-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.