3D Spatial Graph Placement for Home Appliance Layout
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Solution Overview
Problem
Placing bulky and heavy home appliances/furniture in a real space is difficult due to the challenge of determining suitability and optimal arrangement, and existing virtual-reality/augmented-reality technologies lack effective object placement algorithms.
Innovation Solution
An electronic device uses a spatial graph and graph neural network (GNN) to process three-dimensional spatial data and object-related data, adding an empty node for a new object and updating the graph to provide object placement change-related information, including node embedding and edge prediction to determine optimal placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If virtual-reality/augmented-reality technologies are used to visualize object placement, then users can see how objects fit in space, but existing technologies lack effective object placement algorithms to determine optimal arrangement
Solution Approach 1:
The patent segments the object placement problem into discrete graph nodes representing objects and spatial positions, with edges representing spatial relationships. This segmentation transforms the continuous placement optimization problem into a discrete graph structure that can be processed by the GNN algorithm, enabling systematic analysis of all possible placements.
Solution Approach 2:
The patent introduces a graph neural network as an intermediary between the spatial data representation and the placement optimization decision. The GNN acts as a mediator that processes the spatial graph structure, learns optimal placement configurations, and generates placement recommendations, bridging the gap between raw spatial data and optimized placement solutions.
2Ease of operation
If users manually determine optimal placement of bulky objects, then placement decisions can be made, but it is difficult to actually try placing objects in real space and determine suitability
Solution Approach 1:
The patent creates a virtual copy of the real space as a spatial graph representation, allowing users to experiment with object placements in the virtual model without physically moving bulky objects. The GNN processes this virtual spatial graph to generate placement recommendations that accurately reflect real-world spatial constraints, enabling precise placement determination through virtual experimentation.
Solution Approach 2:
The patent performs preliminary processing of spatial data to build the spatial graph structure and pre-compute spatial relationships between existing objects and potential placement locations. This preliminary action prepares the system to quickly evaluate multiple placement scenarios and provide accurate recommendations, saving users time and effort in the actual placement determination process.
3Manufacturing precision
If spatial graph with empty nodes is updated using GNN, then optimal placement recommendations can be generated, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing the GNN processing only on the spatial graph regions affected by the new object placement, rather than reprocessing the entire spatial graph. The empty node representation allows the system to incrementally update only the necessary portions of the spatial graph, reducing computational overhead while maintaining placement recommendation accuracy.
Data Source
AI summary
A method of providing information related to placing an object in a space includes obtaining three-dimensional spatial data corresponding to the space and object-related data for first objects in the space, obtaining a spatial graph including positional relations between the first objects in the space, based on the three-dimensional spatial data and the object-related data, receiving a user input for changing an object placement in the space, based on the user input, adding, to the spatial graph, an empty node representing a second object to be placed in an empty region in the space in which the first objects are not placed, updating the spatial graph by applying, to a graph neural network (GNN), the spatial graph to which the empty node has been added, and outputting object placement change-related information for the space, based on the updated spatial graph.