Interactive Image Reconstruction Without Full 3D Modeling
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Solution Overview
Problem
Existing techniques for generating three-dimensional models or specifications for interactive images are resource-intensive and prohibitive for many applications, necessitating more efficient methods.
Innovation Solution
A content generation platform that merges actually captured content with computer-generated content using neural networks, allowing for the creation of interactive images with partial three-dimensional capabilities through a process involving image capture, processing, and user customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional three-dimensional modeling techniques are used to enable interactive features, then interactivity and completeness of 3D model are improved, but resource consumption and time required increase significantly
Solution Approach 1:
The patent applies partial action by generating only the necessary portions of 3D information needed for interactivity rather than complete models. Neural networks process images to extract sufficient geometric and semantic data for interactive manipulation without constructing full 3D representations, thereby reducing computational resources while maintaining usability.
Solution Approach 2:
The patent uses 2D image data as a copy or approximation of 3D information. Instead of creating actual 3D models, the system generates 2D-based representations that simulate 3D interactivity through techniques like depth estimation and semantic segmentation, providing interactive functionality without the resource cost of true 3D modeling.
2Manufacturing precision
If complete three-dimensional models are generated for interactive features, then model accuracy and interactivity are improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on extracting only the specific 3D attributes necessary for interactivity from images, rather than generating complete and equally accurate 3D models of all scene elements. Neural networks selectively process local features such as object boundaries, depth relationships, and semantic regions to provide sufficient accuracy for interactive manipulation while minimizing overall computational load.
3Adaptability or versatility
If traditional methods are used to create interactive images, then completeness of three-dimensional specification is improved, but complexity and cost of the process increase
Solution Approach 1:
The patent substitutes traditional mechanical 3D scanning and modeling systems with neural network-based image processing. Instead of using physical scanners, cameras, and complex reconstruction algorithms, the system uses deep learning models to directly infer 3D specifications from 2D images, dramatically simplifying the workflow while maintaining adequate 3D completeness for interactive applications.
Data Source
AI summary
A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. An arbitrary input image captured in an unknown environment is processed using one or more machine learning based networks to generate an interactive reconstruction or floorplan of the input image.