AR Synthetic Data Generation for Photorealistic ML Training
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Solution Overview
Problem
Existing machine learning models trained on low-quality synthetic images exhibit poorer performance due to the lack of realism, necessitating more efficient and realistic synthetic data generation methods.
Innovation Solution
Utilizing augmented reality (AR) techniques to generate synthetic training data by positioning virtual objects in 3D scenes, automatically labeling them with metadata, and storing these images in machine learning datasets for improved training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If typical techniques are used to generate synthetic images, then generation cost and time are reduced, but image quality and realism deteriorate
Solution Approach 1:
The patent introduces an AR rendering engine as an intermediary between simple synthetic generation and complex real image collection. This engine composites virtual objects onto real-world backgrounds, creating synthetic images that maintain both generation efficiency and visual realism by combining rendered elements with photorealistic environments.
Solution Approach 2:
The system creates high-quality copies of real-world scenes by capturing actual environments and integrating virtual objects into them. This approach preserves the realism of real images while enabling controlled synthetic data generation, as the background photographs serve as authentic templates that are then augmented with virtual elements.
2Manufacturing precision
If real images are collected and labeled for training, then image quality and realism are maintained, but cost and time consumption increase
Solution Approach 1:
Instead of manually collecting and labeling real images, the system creates synthetic copies by compositing virtual objects onto captured backgrounds. The AR rendering engine automatically generates labeled training data with precise object positions and attributes, eliminating manual annotation while preserving visual realism through photorealistic background integration.
Solution Approach 2:
The system performs self-labeling through the AR rendering process itself. As virtual objects are automatically positioned and rendered onto backgrounds, their metadata (position, orientation, scale) is automatically captured and associated with the generated images, eliminating the need for separate manual labeling operations.
3Productivity
If low-quality synthetic images are used for training, then data generation is faster and cheaper, but machine learning model performance deteriorates
Solution Approach 1:
The AR rendering engine serves as a quality-enhancing intermediary that bridges the gap between synthetic and real images. By compositing virtual objects onto photorealistic backgrounds with proper lighting, shadows, and perspective, the system generates training images that maintain high visual fidelity, ensuring machine learning models learn from realistic data while benefiting from synthetic data generation efficiency.
Data Source
AI summary
The present disclosure is directed to systems and methods for generating synthetic training data using augmented reality (AR) techniques. For example, images of a scene can be used to generate a three-dimensional mapping of the scene. The three-dimensional mapping may be associated with the images to indicate locations for positioning a virtual object. Using an AR rendering engine, implementations can generate an and orientation. The augmented image can then be stored in a machine learning dataset and associated with a label based on aspects of the virtual object.