3D Room Modeling with Semantic Feature Detection
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Solution Overview
Problem
Existing 3D room modeling techniques often result in inaccurate models due to inaccuracies in planar walls and failure to account for features like half walls, windows, doors, and wall openings, leading to incomplete or incorrect representations of physical spaces.
Innovation Solution
A 3D room modeling system that uses semantic understanding of detected surfaces, machine learning techniques, and deep learning algorithms to generate accurate 3D models by classifying and refining surface data, including feature detection and correction through user interface modifications, and employing Lidar and RGB data for precise representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D modeling techniques are used to generate models from 2D images, then the modeling process is simple and fast, but the model accuracy is poor and incomplete representations of physical spaces are produced
Solution Approach 1:
The system segments the 3D modeling process into distinct stages: 2D image capture, semantic feature detection, plane extraction, 3D mesh generation, and feature annotation. Each stage processes specific aspects of the room independently, improving overall accuracy while managing complexity through modular organization of the modeling pipeline
Solution Approach 2:
The system introduces intermediate data structures including semantic feature annotations, extracted plane data with mathematical representations, and structured 3D mesh formats as intermediaries between 2D image input and final 3D model output. These intermediaries enable progressive refinement of model accuracy without requiring the entire system to be overly complex
2Reliability
If basic 3D mesh generation is performed without semantic feature detection, then the processing time is short and the system is simple, but important features like doors, windows, and half walls are missed
Solution Approach 1:
The system performs preliminary semantic feature detection and classification during the 2D image processing stage, identifying doors, windows, half walls, and other features before 3D mesh generation. This preliminary action ensures features are not missed in the final model while optimizing processing time by preparing feature data in advance
Solution Approach 2:
The system incorporates feedback mechanisms where detected semantic features are used to guide and refine the 3D mesh generation process. Feature detection results feed into plane extraction and mesh construction stages, allowing the system to adjust modeling parameters based on identified features, thereby improving reliability without excessive time loss
3Loss of information
If detailed feature annotation is performed during 3D modeling, then the model completeness is improved, but the complexity of the system increases
Solution Approach 1:
The system applies local quality by providing detailed semantic annotations and feature-specific data only where needed in the 3D model, rather than uniformly across the entire model. Important features like doors and windows receive detailed annotation while other areas use standard mesh representation, reducing overall system complexity while maintaining information completeness
Solution Approach 2:
The system employs universal data structures and annotation formats that can represent multiple feature types (doors, windows, half walls, regular walls) within a single unified framework. This multi-functionality allows comprehensive feature information to be captured without requiring separate complex systems for each feature type
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 simulations and mapping of physical spaces without the need for actual furniture placement, with improved accuracy in detecting and representing doors, windows, and other room features.
Implementation Method 1
employing Lidar and RGB data for precise representation
Data Source
AI summary
Devices and techniques are generally described for three dimensional room modeling. In various examples, 3D mesh data representing a room may be received. Plane data comprising a plurality of planes may be received. Each plane of the plurality of planes may represent a planar surface detected in the room. In some cases, a first plurality of wall candidates for a 3D model of the room may be determined based at least in part on the plane data. A second plurality of wall candidates for the 3D model of the room may be determined by modifying the first plurality of wall candidates based on a comparison of the first plurality of wall candidates to the 3D mesh data. The 3D model of the room may be generated based at least in part on the second plurality of wall candidates.