Unsupervised Scene Structure Learning for Synthetic Data
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Solution Overview
Problem
Current approaches for generating synthetic datasets for realistic virtual environments are hindered by the complexity of tuning procedural models and the need for extensive manual effort, which limits the realism and diversity of generated scenes, and often require large amounts of annotated ground truth data that is cumbersome to obtain.
Innovation Solution
A procedural generative model learns unsupervised from real imagery to optimize scene parameters and structure, using a probabilistic scene grammar and reinforcement learning to align generated scenes with real data distributions, allowing for the creation of synthetic scenes without manual annotation and ground truth labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tuning of procedural model parameters is performed to generate realistic scenes, then scene realism can be improved, but the complexity and time required for parameter configuration increases significantly
Solution Approach 1:
The system performs self-service by automatically learning scene parameters and procedural models from unannotated real images without requiring manual expert tuning. The neural network autonomously extracts scene structure, object relationships, and spatial configurations, eliminating the need for manual parameter configuration while maintaining high scene realism.
Solution Approach 2:
The patent replaces the manual mechanical tuning process with an automated neural network-based system. Instead of experts manually adjusting procedural model parameters, the system uses machine learning to automatically learn and optimize scene generation parameters from visual data, substituting human expertise with automated computational methods.
2Measurement precision
If extensive manual annotation of ground truth data is performed to train scene generation models, then training accuracy can be improved, but the time and resources required for data preparation increases significantly
Solution Approach 1:
The system performs self-service by learning directly from unannotated real images without requiring manual ground truth labels. The neural network autonomously discovers scene structures, object semantics, and spatial relationships through self-supervised learning, eliminating the time-consuming process of manual data annotation while maintaining training effectiveness.
Solution Approach 2:
The system creates synthetic copies of real scenes through procedural generation guided by learned parameters. Instead of requiring annotated real data, the model learns to copy scene structures and relationships from unannotated images and generates synthetic training data automatically, reducing dependency on manually prepared ground truth datasets.
3Ease of operation
If simple procedural models are used for scene generation to reduce complexity, then ease of configuration is improved, but the diversity and realism of generated scenes deteriorates
Solution Approach 1:
The patent transforms static procedural models into dynamic, adaptive systems. The procedural models are no longer fixed but are automatically learned and adjusted by neural networks based on the target scene characteristics. This allows the models to adapt to diverse scene types and configurations automatically, maintaining simplicity while achieving high diversity and realism through learned parameter optimization.
Solution Approach 2:
The system automatically optimizes procedural model parameters through neural network learning. Instead of using fixed simple parameters, the system learns and adjusts parameters such as object densities, spatial distributions, and scene compositions to match target scenes. This parameter optimization enables simple procedural models to generate diverse and realistic scenes without increasing configuration complexity.
Data Source
AI summary
A rule set or scene grammar can be used to generate a scene graph that represents the structure and visual parameters of objects in a scene. A renderer can take this scene graph as input and, with a library of content for assets identified in the scene graph, can generate a synthetic image of a scene that has the desired scene structure without the need for manual placement of any of the objects in the scene. Images or environments synthesized in this way can be used to, for example, generate training data for real world navigational applications, as well as to generate virtual worlds for games or virtual reality experiences.