Synthetic Scene Graph Generation for Realistic Neural Network Training
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Solution Overview
Problem
Conventional methods for generating synthetic data for neural network training face challenges in creating environments that accurately resemble real-world scenes, failing to bridge the domain and content gaps between synthetic and real-world data, which hinders the effectiveness of downstream task networks.
Innovation Solution
A generative model, such as a graph convolutional network (GCN), transforms initial scene graphs to update attributes, aligning their distributions with real-world environments, and uses a renderer to generate synthetic datasets that closely mimic real-world scenes, enabling effective training of downstream task networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If domain randomization is used to generate synthetic data, then the amount of diverse scenes is increased, but the realism and accuracy of the synthetic environments deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the attributes of objects in synthetic scenes (such as location, pose, color, texture, shape, size) to better match real-world distributions. This involves adjusting parameters like object positions, orientations, and visual properties to bridge the domain gap between synthetic and real environments, thereby improving accuracy while maintaining diverse scene generation capability
2Manufacturing precision
If GANs are used to transform appearance of synthetic data, then photo-realism is improved, but the content accuracy and realism of object attributes deteriorates
Solution Approach 1:
The patent introduces an intermediary approach by using a renderer that generates synthetic images from transformed scene graphs. This renderer acts as a mediator between the transformed scene graph representations and the final synthetic images, ensuring that both photo-realism and attribute accuracy are maintained. The renderer processes transformed scene graphs to produce images that preserve the accuracy of object attributes while achieving photo-realistic appearance
3Manufacturing precision
If manual creation of virtual worlds is used, then the quality of synthetic environments is improved, but the time and labor required increases
Solution Approach 1:
The patent applies copying by using scene graphs as a structured representation to generate synthetic environments automatically. Instead of manually creating virtual worlds, the system copies the structure and attributes from scene graph representations to generate diverse scenes efficiently. This automated copying process from scene graphs to synthetic environments maintains quality while significantly reducing time and labor requirements
Data Source
AI summary
In various examples, a generative model is used to synthesize datasets for use in training a downstream machine learning model to perform an associated task. The synthesized datasets may be generated by sampling a scene graph from a scene grammar—such as a probabilistic grammar— and applying the scene graph to the generative model to compute updated scene graphs more representative of object attribute distributions of real-world datasets. The downstream machine learning model may be validated against a real-world validation dataset, and the performance of the model on the real-world validation dataset may be used as an additional factor in further training or fine-tuning the generative model for generating the synthesized datasets specific to the task of the downstream machine learning model.