AR Surface Detection for Dynamic Product Presentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for augmented reality (AR) in home decor shopping require customers to manually select products and surfaces, leading to an inconvenient experience, as they do not accurately distinguish between similar surfaces or recognize product suitability, resulting in inappropriate virtual decor displays.
Innovation Solution
A system that uses camera feeds to analyze surroundings, identify suitable surfaces, and recommend products based on surface attributes, orientation, and customer preferences, employing methods like SLAM, product classification, and sensor data to provide accurate and context-aware AR product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers manually select products and surfaces in current AR systems, then the system can display virtual decor, but the customer experience is inconvenient and accuracy is poor
Solution Approach 1:
The system automatically identifies surfaces and recommends suitable products without requiring manual customer selection. The AR system captures images, processes them through machine learning models to detect surfaces and their attributes, and autonomously generates product recommendations, allowing the system to serve itself rather than requiring continuous user input.
Solution Approach 2:
The patent replaces manual mechanical selection processes with automated computer vision and machine learning systems. Instead of customers manually selecting surfaces and products, the system uses image processing, SLAM technology, and classification algorithms to automatically identify surfaces and recommend appropriate decor items.
2Adaptability or versatility
If the system displays all products on all surfaces, then product availability is high, but relevance and appropriateness decrease
Solution Approach 1:
The system tailors product recommendations to specific local characteristics of each detected surface. By analyzing surface attributes such as type, orientation, size, and environmental context, the system provides customized product suggestions that are locally appropriate for each surface rather than displaying generic product catalogs.
Solution Approach 2:
The system dynamically adjusts product recommendations based on detected surface parameters including orientation (horizontal/vertical), size dimensions, surface type (floor, wall, table), and environmental context. These parameter changes enable the system to filter and rank products according to their suitability for each specific surface configuration.
3Measurement precision
If the system distinguishes between similar surfaces using advanced analysis, then product recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of surface identification into distinct processing stages: image capture, initial surface detection, attribute extraction (type, orientation, size), context analysis, and product recommendation. This segmentation allows each component to specialize in specific functions, managing overall system complexity while achieving high differentiation accuracy.
Solution Approach 2:
The patent introduces intermediate processing layers including machine learning classification models and surface attribute extraction systems that act as mediators between raw image data and final product recommendations. These intermediaries translate complex visual information into structured surface characteristics that can be efficiently used for product matching.
Data Source
AI summary
Methods and systems for providing a dynamic product presentation are disclosed. In one example, a method comprises identifying, by a processor, a first surface in a first view of a camera feed from a customer device; obtaining, by the processor, a three-dimensional model of a product that corresponds to the first surface; providing, by the processor, an augmented media containing an overlay of a first augmented reality representation of the three-dimensional model of the product in the first view; and responsive to the processor identifying a second surface in a second view of the camera feed from the customer device, revising, by the processor, the augmented media to contain a second augmented reality representation of the three-dimensional model of the product on the second surface in the second view.