2D Image Processing for 3D Model Generation and 2D Floor Plan Creation
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Solution Overview
Problem
Conventional methods for generating 2D floor plans require manual user input and may be cumbersome, leading to inaccuracies, especially when using non-LiDAR technology like smartphones.
Innovation Solution
The use of a computer system that employs 2D image processing to generate a 3D model of a room, which is then corrected and projected onto a 2D plane to create an accurate floor plan, leveraging machine learning models for semantic segmentation and trajectory-based room delineation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual user input methods are used to generate 2D floor plans, then the process can be completed with simple technology, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system automatically captures images, generates 3D models, extracts 2D floor plans, and detects furniture objects without requiring manual user input for each step. The processing system serves itself by autonomously completing the entire floor plan generation workflow from raw images to final annotated plans.
Solution Approach 2:
The system performs preliminary 3D model generation and 2D projection before furniture detection. By pre-processing the images to create 3D models and initial 2D floor plans, the system prepares the data structure in advance, making the subsequent furniture detection more efficient and accurate.
2Device complexity
If non-LiDAR technology like smartphones is used, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The system replaces LiDAR-based mechanical 3D scanning with 2D image-based computational 3D reconstruction. Instead of using light detection and ranging hardware, the system uses multiple 2D images captured by standard smartphone cameras and reconstructs 3D models through image processing and machine learning algorithms.
Solution Approach 2:
The system transforms 2D image parameters into 3D spatial parameters through computational methods. By changing the parameter representation from 2D coordinates to 3D model data, the system achieves accurate measurements using standard camera hardware that would otherwise only capture 2D information.
3Ease of manufacture
If conventional 2D plan generation methods are used, then the process is simple, but the accuracy of the floor plan deteriorates
Solution Approach 1:
The system introduces a 3D intermediate dimension to improve 2D floor plan accuracy. By generating 3D models from 2D images and then projecting back to 2D, the system uses the additional spatial dimension to capture depth information and geometric relationships that would be lost in direct 2D processing, thereby improving measurement accuracy.
Data Source
AI summary
Techniques for two-dimensional (2D) image processing to generate a three-dimensional (3D) model and determine a 2D plan are described herein. In an example, a 3D model of a room can be generated by using a video file portion of a video file as a first input to a first machine learning (ML) model. Semantic segmentation of the room can be generated by using the video file portion as a second input to a second ML model. The semantic segmentation may indicate that an object having an object type is shown in a first image frame of the video file portion. A 3D representation of the object in the 3D model can be determined. The 3D model can be corrected by setting a property of the 3D representation to a predefined value. A 2D floor plan of the room can be generated based on the corrected 3D model.