Room Geometry Reconstruction Using Parametric Base Elements
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Solution Overview
Problem
Current computer-implemented methods for modeling interior rooms face challenges in accurately reconstructing and visualizing complex geometries and objects within these spaces, particularly in generating high-quality virtual representations that allow for user interaction and modification.
Innovation Solution
A computer-implemented method that utilizes sensor data from image sensors to create a point cloud of an interior room, which is then represented using base elements with predefined geometry, optimizing the spatial arrangement of these elements to minimize the number required for accurate representation, allowing for iterative refinement and user feedback-driven modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If base elements are used to represent point cloud data for room reconstruction, then the accuracy of geometry representation is improved, but the number of base elements required increases
Solution Approach 1:
The patent segments the room geometry into multiple base elements with predefined geometries (planes, cylinders, spheres, etc.). Each base element represents a portion of the point cloud data, allowing accurate reconstruction while maintaining manageable complexity through systematic division of the spatial domain
Solution Approach 2:
The patent optimizes parameters of base elements (position, orientation, size, geometry type) to achieve accurate representation with minimal elements. By changing and adjusting these parameters iteratively, the system reduces the number of base elements needed while maintaining reconstruction accuracy
2Productivity
If iterative optimization is performed to minimize base elements, then the efficiency of reconstruction is improved, but the computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining a set of base element geometries and their parameters before processing the point cloud data. This preparation allows the iterative optimization to focus only on selecting and positioning elements rather than creating them from scratch, reducing overall computational time
Solution Approach 2:
The patent implements feedback mechanisms during iterative optimization where the system evaluates the representation quality of base elements against the point cloud data and adjusts parameters accordingly. This feedback loop enables efficient convergence to optimal solutions by guiding the optimization process toward accurate representations with minimal elements
Data Source
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AI summary
A computer-implemented method of reconstructing an interior room is described, wherein sensor data indicative of at least one image of at least a part of a room acquired with at least one image sensor of a user device is received, and wherein at least a part of a point cloud representative of the at least part of the room and/or representative of one or more objects arranged in the room is determined. Further, at least a part of a geometry of the room is reconstructed based on representing at least a portion of the point cloud with one or more base elements associated with one or more parametric models of predefined geometry and/or shape.