Virtual Space Defurnishing and Refurnishing for Interactive Walkthroughs
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Solution Overview
Problem
Users are unable to interact with virtual environments and visualize alternative furniture or décor in real estate walkthroughs, leading to guesswork in imagining how spaces would look without existing furnishings.
Innovation Solution
A system and method using machine learning models to identify and mask interior elements in digital models, allowing users to select design styles and add or remove furnishings, and generate 2D or 3D representations of spaces with desired furnishings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users navigate through virtual walkthroughs of furnished properties, then they can view the property layout and existing furnishings, but they cannot interact with the environment or visualize alternative furniture arrangements
Solution Approach 1:
The system creates virtual copies of furniture and decor elements that can be manipulated in the digital environment. Users can select from catalogs of furniture items and place them in the virtual space, effectively copying physical objects into the digital realm for visualization and interaction purposes.
Solution Approach 2:
The system introduces an intermediary layer between the user and the virtual environment - a computational interface that processes user selections, performs geometric optimizations, and renders the modified scenes. This intermediary enables interaction without requiring direct manipulation of the underlying 3D model data.
2Loss of information
If users attempt to imagine spaces without furniture or with alternative furnishings, then they can conceptualize different designs, but they must rely on guesswork regarding dimensions and placement
Solution Approach 1:
The system performs preliminary geometric optimization of the 3D model before allowing user interaction. This pre-processing step ensures that the virtual space has accurate dimensions and proper geometric relationships, eliminating the need for users to guess spatial parameters during the design process.
Solution Approach 2:
The system replaces manual measurement and spatial estimation with automated computational geometry processing. The backend system handles complex 3D spatial calculations, dimension verification, and geometric optimizations, substituting mechanical estimation with digital precision.
3Measurement precision
If the system provides detailed 3D representations of spaces, then users can see accurate spatial information, but the complexity of manipulating and visualizing these representations increases
Solution Approach 1:
The system segments the complex 3D manipulation task into distinct functional layers: data acquisition, geometric optimization, furniture catalog selection, placement operations, and rendering. This segmentation allows each layer to be optimized independently while maintaining overall system simplicity for the end user.
Solution Approach 2:
The system creates a universal interface that handles multiple functions - viewing the space, selecting furniture, placing items, and visualizing modifications - through a unified user experience. The same interface adapts to different user needs whether they want to remove furniture, add new items, or rearrange existing pieces.
Data Source
AI summary
An example system may access data of a multidimensional space representing a physical environment and identify interior elements within the multidimensional space using a first machine learning model. The interior element may represent furniture in the physical environment. The system may mask one or more of the interior elements with masks and fill each of the masks with imagery of the physical environment to create an appearance of a defurnished space, the defurnished space having the one or more interior elements appearing as missing from the multidimensional space representing the physical environment. The system may provide all or some of the defurnished space for display.


