3D Model Generation from Images Using Semantic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image-based modeling techniques face challenges in generating photo-realistic three-dimensional models, particularly at ground level, due to significant user interaction requirements and scaling difficulties, which hinder large-scale modeling applications.
Innovation Solution
The proposed solution involves a multi-view semantic segmentation method that recognizes and segments images at a pixel level into semantically meaningful areas, employing an inverse patch-based orthographic composition and structure analysis to regularize noisy data, and imposing strong priors of building regularity to produce visually compelling 3D models automatically or semi-automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image-based modeling techniques are used, then user interaction is required for model generation, but this significantly increases operation complexity and reduces productivity for large-scale modeling
Solution Approach 1:
The system performs automatic multi-view image stitching and 3D model generation without requiring manual user intervention. The automated pipeline includes image registration, feature extraction, model reconstruction, and texture mapping, all executed automatically to generate photo-realistic 3D models of buildings and cityscapes
Solution Approach 2:
Manual mechanical operations (user interaction for model generation) are replaced with automated computational processes. The system uses computer vision algorithms, machine learning models, and automated rendering pipelines to substitute human operations, enabling large-scale modeling at ground level
2Extent of automation
If automated methods focus on early stages of modeling pipeline, then automation extent increases, but manufacturing precision (model quality) remains unsatisfactory for graphics applications
Solution Approach 1:
The system incorporates feedback mechanisms where rendered 3D models are evaluated against reference images and user preferences. The feedback loop enables iterative refinement of model geometry, texture, and lighting to achieve photo-realistic quality while maintaining full automation throughout the pipeline
3Ease of operation
If conventional single-view methods are used, then ease of operation improves, but manufacturing precision deteriorates due to inability to capture true 3D structure
Solution Approach 1:
The system segments the 3D modeling task into multiple views and processes each view independently through automated pipelines. Multiple images from different angles are stitched together and processed through separate computational stages, then integrated to produce accurate 3D models, combining operational simplicity with high precision
4Measurement precision
If manual feature selection and correspondence indication are required, then measurement precision improves, but productivity decreases due to tedious operations
Solution Approach 1:
The system automatically performs feature detection, extraction, and correspondence matching across multiple images. Computer vision algorithms identify and track features such as corners, edges, and landmarks without human intervention, achieving high measurement precision through automated algorithms that process hundreds of images efficiently
Data Source
AI summary
The subject disclosure relates to generating models from images. In an aspect, multi-view semantic segmentation is provided to recognize and segment images at the pixel level into semantically meaningful areas, and which can provide labels with a specific object class. In further aspects, a partition scheme is provided that can separate objects into independent blocks using major line structures of a scene. In addition, an inverse patch-based orthographic composition and structure analysis on a block is provided that can regularize noisy and missing reconstructed 3D data to facilitate image-based modeling.