Semi-supervised Interior Layout Estimation from Panoramic Images
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Solution Overview
Problem
Current methods for inferring the layout of interior spaces from digital images are hindered by the need for extensive labeled data, which is difficult to obtain due to the complexity of interior spaces and the subjective nature of annotations, and existing deep learning approaches have not effectively leveraged unlabeled data for more challenging tasks like layout estimation.
Innovation Solution
A semi-supervised approach using a combination of labeled and unlabeled data to train models for layout estimation, where a neural architecture learns to identify junctures in interior spaces from panoramic images, allowing for accurate modeling with as few as 20 labeled examples and matching the performance of fully supervised models with a small number of labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for layout estimation, then measurement precision is improved, but the quantity of labeled data required increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on synthetic rendered images before fine-tuning on real labeled images. This pre-training step prepares the model in advance to handle complex layout estimation tasks, reducing the amount of real labeled data needed for achieving high precision.
Solution Approach 2:
The patent uses synthetic rendered images as copies of real interior spaces to create training data. These synthetic copies allow the model to learn from diverse virtual environments without requiring extensive real-world annotated data, thereby reducing dependency on large quantities of labeled real images.
2Measurement precision
If manual annotation is performed to obtain labeled data, then measurement precision is improved, but loss of time increases due to the complexity of interior spaces
Solution Approach 1:
The patent generates synthetic training data by rendering virtual copies of interior spaces with automatically generated annotations. This eliminates the need for manual annotation of real images, saving significant time while maintaining annotation precision through controlled synthetic data generation.
Solution Approach 2:
The system performs self-annotation by automatically generating labels for synthetic rendered images through programmatic processes. This self-service annotation approach eliminates manual labor while providing precise, consistent annotations that would be difficult and time-consuming to obtain through human annotators.
3Measurement precision
If more labeled data is collected to improve model performance, then measurement precision is improved, but device complexity increases due to data management requirements
Solution Approach 1:
The patent uses synthetic rendered images as substitutes for large volumes of real labeled data. This approach reduces data management complexity by eliminating the need to collect, store, and manage extensive datasets of real annotated images, while still achieving high model performance through the generated synthetic data.
4Ease of operation
If traditional measuring implements are used, then ease of operation is maintained, but measurement precision deteriorates due to occlusion and occupancy issues
Solution Approach 1:
The patent replaces traditional mechanical measuring implements with a computer vision-based neural network system. This substitution maintains ease of operation by using simple image capture while dramatically improving measurement precision by analyzing panoramic images to infer layout, avoiding issues with physical occlusion and occupancy that plague manual measuring.
Data Source
AI summary
Introduced here computer programs and associated computer-implemented techniques for modeling interior spaces based on an analysis of digital images of those interior spaces. These computer programs can be trained to accomplish this without using extensive sets of labeled data. Instead, these computer programs are able to effectively supplement labeled data with unlabeled data to accurately model interior spaces in settings where limited labeled data is available. Such an approach allows these computer programs to establish the layouts of interior spaces without extensive knowledge of those interior spaces and without users, who are responsible for generating the digital images, interacting with the surroundings in a meaningful way.