3D Room Modeling Interface with Semantic Correction
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Solution Overview
Problem
Existing 3D room modeling techniques often result in inaccuracies, such as misclassified planar walls and failure to account for features like half walls, windows, and doors, leading to incomplete or incorrect models of physical spaces.
Innovation Solution
A 3D room modeling system that employs semantic understanding of detected surfaces, using machine learning techniques and Lidar sensors to generate accurate 3D models by classifying and refining surface data, including feature detection and correction tools for users to modify detected features like doors and windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated 3D modeling is used to generate models from 2D images, then productivity is improved, but measurement precision deteriorates due to misclassified walls and missed features
Solution Approach 1:
The system implements a feedback mechanism where the automated modeling process generates initial 3D models, which are then reviewed and corrected by users through the interface. The corrected models feed back into the system to improve future automated modeling accuracy, resolving the contradiction between speed and precision by allowing rapid initial generation followed by targeted refinement.
Solution Approach 2:
The patent introduces an intermediary layer between automated modeling and final output: a user interface that acts as a mediator. This intermediary allows users to review, select, and correct detected features (walls, windows, doors) without requiring complete manual remodelling, thus maintaining productivity while improving measurement precision through human-in-the-loop validation.
2Measurement precision
If comprehensive feature detection is performed to identify all room elements, then measurement precision is improved, but device complexity increases due to multiple sensors and processing requirements
Solution Approach 1:
The patent employs a multi-functional camera system that serves multiple purposes: capturing 2D images for semantic analysis, providing visual feedback to users through the interface, and documenting the space. This universal device reduces system complexity compared to using separate specialized sensors for each function while maintaining comprehensive feature detection capability.
Solution Approach 2:
The system implements self-service through automated semantic analysis that automatically detects and classifies room features (walls, windows, doors) without requiring manual intervention. This automation reduces the complexity burden on the user while maintaining high measurement precision through advanced image processing algorithms.
3Measurement precision
If manual correction tools are provided for user feedback, then measurement precision is improved, but ease of operation deteriorates due to additional user workload
Solution Approach 1:
The patent uses copying by generating initial 3D model copies and feature detections that users can review and correct. Rather than requiring users to create models from scratch, the system provides pre-generated copies that need only minor adjustments, significantly reducing user effort while maintaining high accuracy through selective correction.
Solution Approach 2:
The system applies partial action by allowing users to correct only the specific features that need improvement rather than requiring complete manual verification of all elements. Users can selectively review and correct walls, windows, and doors based on confidence levels or specific concerns, reducing overall user effort while maintaining measurement precision for critical features.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system produces accurate 3D models of rooms that include detailed features, allowing for precise interior design and mapping, with user-friendly interfaces for correcting model inaccuracies and enhancing feature detection accuracy.
Implementation Method 1
using machine learning techniques and Lidar sensors to generate accurate 3D models
Data Source
AI summary
Devices and techniques are generally described for three dimensional room modeling. In various examples, a three-dimensional (3D) room model comprising at least a first wall and a floor may be received. A first sphere sized and shaped such that the 3D room model fits within the first sphere may be determined and a virtual camera may be positioned on the first sphere at a first position. A first command may be received to move the virtual camera in a first direction. The virtual camera may be translated in the first direction along a surface of the first sphere to a second position. A view of the interior of the 3D room model may be displayed from a second viewpoint of the virtual camera at the second position.