Home Recommendation Systems Using NeRF for Interactive 3D Exploration
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Solution Overview
Problem
Current virtual home listings and remodeling options are limited, failing to provide qualitative or quantitative fixtures important to home buyers and remodelers, and do not allow for interactive exploration of homes based on subjective interests, leading to inefficient home-buying and remodeling experiences.
Innovation Solution
A home recommendation computing system utilizing machine learning models, such as Neural Radiance Fields (NeRF), to generate dynamic 3D models from 2D images, allowing users to explore homes from arbitrary viewpoints and receive personalized recommendations for purchase or remodeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional virtual home listings are used, then home buying process is simplified, but interaction and exploration capability is limited
Solution Approach 1:
The system creates virtual copies of physical homes using 3D modeling and Neural Radiance Fields technology. These digital replicas preserve the visual and spatial characteristics of the original properties, allowing users to interact with and explore homes remotely without physically visiting them, thus maintaining ease of operation while dramatically improving interactive exploration capabilities.
Solution Approach 2:
The patent transitions from traditional 2D photo-based listings to immersive 3D virtual environments. By adding the third dimension and enabling spatial navigation, users can explore homes from multiple angles and perspectives, significantly enhancing interaction and exploration capability while keeping the interface user-friendly through intuitive navigation controls.
2Measurement precision
If detailed home features are provided, then recommendation accuracy improves, but data processing complexity increases
Solution Approach 1:
The system employs machine learning models and Neural Radiance Fields that automatically process and analyze captured images and user interactions. The AI algorithms self-learn from the data, extracting relevant features and patterns without requiring manual intervention, thus improving recommendation accuracy while managing data processing complexity through automation.
Solution Approach 2:
The patent transforms visual data from images into structured 3D spatial parameters and features that can be efficiently processed by machine learning models. By converting unstructured image data into meaningful geometric and visual parameters, the system achieves high recommendation accuracy while optimizing data processing efficiency through appropriate parameter representation.
3Reliability
If 3D models are generated from images, then home exploration realism improves, but computational resources required increase
Solution Approach 1:
The system generates 3D models using Neural Radiance Fields that can be created from a limited set of 2D images rather than requiring complete 360-degree scans or extensive photogrammetry data. This partial action approach achieves sufficient realism for home exploration while dramatically reducing the computational resources and time required compared to traditional full 3D scanning methods.
Data Source
AI summary
In one aspect, an example method includes: (a) capturing a plurality of images of a home environment; (b) receiving a user input, wherein the user input is associated with the captured plurality of images of the home environment; (c) generating a home recommendation model using one or more machine learning models, wherein the one or more machine learning models are configured to generate the home recommendation model using the captured plurality of images of the home environment and the received user input; (d) identifying one or more home recommendations, wherein the one or more home recommendations are based on at least the generated home recommendation model; and (e) transmitting instructions that cause a computing device to display, via a user interface of the computing device, a graphical indication of the one or more home recommendations.