Synthetic Data Generation for Machine Learning Model Training
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Solution Overview
Problem
Current methods for generating synthetic datasets for machine learning models face challenges such as high costs and inefficiencies due to the need for extensive human labeling, limited variability in virtual worlds, and poor image quality, leading to inadequate coverage of the parameter space and domain shift issues between synthetic and real-world data.
Innovation Solution
A method that determines sets of parameter values for geometric, rendering, and augmentation parameters to generate realistic, intrinsically labeled synthetic images, maximizing coverage and variation within a multidimensional parameter space, using low-discrepancy sequences for efficient sampling and procedural generation of 3D scenes, which are then rendered and augmented to create diverse and high-quality datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world datasets are collected for machine learning, then the data covers real-world scenarios, but the labeling process is time-consuming and expensive
Solution Approach 1:
The patent creates synthetic copies of real-world scenes using 3D virtual worlds that replicate environmental parameters, objects, and spatial relationships. These synthetic images serve as substitutes for real-world data while eliminating the need for human labeling, as the ground truth is automatically known from the virtual scene construction.
Solution Approach 2:
The system generates its own labeled data through procedural generation of 3D scenes. The virtual camera captures images from known positions and angles, automatically providing precise ground truth annotations without requiring external human annotators. The scene graph and camera parameters self-provide the labeling information.
2Extent of automation
If synthetic datasets are generated using virtual worlds, then labeling is automated, but the parameter space coverage is limited
Solution Approach 1:
The patent implements dynamic adjustment of virtual camera parameters including position, orientation, focal length, and exposure settings. The 3D scene content is procedurally generated with varying objects, materials, lighting conditions, and environmental parameters. This dynamic variability allows the system to cover extensive parameter spaces while maintaining automated labeling through the known virtual scene construction.
3Extent of automation
If virtual camera is used to generate synthetic images, then data generation is automated, but image quality and realism are poor
Solution Approach 1:
The system varies multiple rendering parameters including camera exposure, focal length, aperture, depth of field, lighting conditions, and material properties to generate diverse synthetic images. The procedural generation system creates different 3D scenes with varying geometric parameters, object arrangements, and environmental conditions, producing high-quality images that maintain realism while preserving automated generation benefits.
4Device complexity
If limited virtual worlds are used, then the system is simpler, but domain shift between synthetic and real data increases
Solution Approach 1:
The patent employs a universal 3D scene generation framework that can create diverse virtual worlds with varying environmental parameters, object types, and spatial configurations. This multi-functional system generates synthetic data across multiple domains and scenarios, reducing domain shift by exposing models to a broader range of conditions that better match real-world diversity while maintaining manageable system complexity through procedural generation.
Data Source
AI summary
A method and system for synthetic data generation and analysis includes generating a synthetic dataset. A set of parameters is determined and scenarios are generated from the parameters that represent three-dimensional scenes. Synthetic images are rendered for the scenarios. A synthetic dataset may be formed to have a controlled variation in attributes of synthetic images over a synthetic dataset. The synthetic dataset may be used for training or evaluating a machine learning model.