Neural Network Lens Attribute Prediction for 3D Reconstruction
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Solution Overview
Problem
Existing AI technologies struggle to accurately predict camera lens attributes from images, which is essential for combining multiple images into one and generating accurate 3D models from 2D images, especially in applications like augmented, mixed, and virtual reality, where camera settings like focal length are critical for proper spatial alignment.
Innovation Solution
A neural network system is trained on datasets that include both visible and invisible keypoints to predict camera lens attributes, such as focal length, by establishing correspondence between keypoints from different angles and using a mathematics equation involving a camera lens calibration matrix to calculate the attribute, enabling the production of reconstructed 3D geometry from 2D images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network is trained to predict camera lens attributes from images, then the ability to generate 3D models and combine images is improved, but the accuracy of prediction remains insufficient with existing AI technologies
Solution Approach 1:
The system segments the lens attribute prediction problem into multiple independent predictions for different image regions. A neural network predicts lens attributes separately for each region, and these predictions are then aggregated to form the final result. This segmentation allows the system to capture spatial variations in lens effects across the image, improving prediction accuracy while maintaining the ability to generate accurate 3D models.
2Adaptability or versatility
If camera metadata is not applied, then image processing flexibility is maintained, but the ability to combine multiple images into one and produce accurate 3D models is lost
Solution Approach 1:
The system enables images to self-describe their lens attributes through region-based neural network predictions. Instead of relying on external metadata that may be missing or inaccurate, each image region automatically predicts its own lens characteristics (focal length, aperture, etc.). This self-service mechanism allows the system to combine multiple images with different lens settings while maintaining processing flexibility and achieving accurate 3D model generation.
Data Source
AI summary
A system, apparatus, and method for predicting a camera lens attribute using a neural network are presented. The predicted camera lens parameter may be used to produce a 3D model of an object from one or more 2D images of the object.