Computer-Vision Viewfinder Guidance for 3D Feature Correspondence
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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 that provides viewfinder interfaces and feedback to guide improved image capture, detects features and feature correspondences, and generates metrics to enhance the quality and number of feature matches, using intra- and inter-image parameter evaluation systems to optimize image framing and capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer-vision techniques are executed on 2D images to generate 3D models, then 3D model reconstruction can be achieved, 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 users to recapture images before proceeding with reconstruction
Solution Approach 2:
The system provides feedback to users about image quality metrics and specific deficiencies (low resolution, blur, occlusion, missing feature correspondences), enabling users to improve their capture technique and recapture images that meet the required quality standards
2Measurement precision
If users capture images manually without guidance, then image capture is simple and quick, but the quality and number of feature correspondences are insufficient for effective 3D reconstruction
Solution Approach 1:
The system provides real-time feedback during the image capture process, evaluating each image's suitability for 3D reconstruction and guiding users on how to improve their captures through specific instructions about framing, angle, and quality requirements
Solution Approach 2:
The system establishes quality criteria and evaluation metrics before the capture process begins, allowing users to understand the requirements upfront and capture images that meet the standards from the start, reducing the need for recaptures
3Productivity
If low resolution photos are used, then image capture is faster and requires less computational resources, but the effectiveness of computer-vision techniques and 3D reconstruction is reduced
Solution Approach 1:
The system evaluates image resolution and other quality parameters before proceeding with computationally intensive 3D reconstruction, filtering out low-quality images that would waste computational resources and preventing poor-quality reconstructions
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.