Synthetic Multi-Modal Image Generation for Object Detection Training
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Solution Overview
Problem
Deep learning technologies for object detection in RGB-Depth images require a substantial amount of training data, which can be a limitation in scenarios where data collection is challenging or expensive.
Innovation Solution
A system that generates synthetic multi-modal-image pairs by combining object models, textures, background planes, and illumination maps, allowing for the creation of diverse training datasets without the need for extensive real-world data collection. This involves selecting object models, adding textures and background images, and generating depth and illumination-map images within defined parameters to simulate various scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning technologies are used for object detection in RGB-Depth images, then detection performance is improved, but the requirement for training data quantity increases
Solution Approach 1:
The patent uses 3D object models to generate synthetic depth images that copy the essential structural information of real objects. These synthetic images serve as training data substitutes, allowing deep learning models to be trained without requiring large quantities of real RGB-Depth image pairs. The copying principle enables faithful reproduction of object geometry and depth characteristics while eliminating the need for extensive real-world data collection.
Solution Approach 2:
The system varies multiple parameters including object model selections, texture assignments, background configurations, and camera positioning to generate diverse synthetic training data. By changing these parameters systematically, the patent creates a wide variety of training scenarios that maintain the structural integrity needed for deep learning while reducing dependency on real data quantity.
2Measurement precision
If real-world data collection is performed to improve training data quality, then detection accuracy is improved, but time and resource consumption increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing 3D object models with textures and placing them in virtual scenes with configured backgrounds and lighting conditions before generating the training data. This preliminary setup in the virtual environment eliminates the need for time-consuming real-world data collection, while still producing high-quality synthetic training data that maintains the precision needed for accurate object detection.
3Adaptability or versatility
If diverse training scenarios are created to improve model adaptability, then detection versatility is improved, but system complexity increases
Solution Approach 1:
The patent employs a universal 3D modeling framework that can generate multiple training scenarios using the same core system. The versatile object models and configurable parameters allow a single system to produce diverse training data for various object categories and environmental conditions, achieving high model adaptability without proportionally increasing system complexity. The multi-functionality principle enables one system to serve multiple training needs.
Data Source
AI summary
Devices, systems, and methods obtain an object model, add the object model to a synthetic scene, add a texture to the object model, add a background plane to the synthetic scene, add a support plane to the synthetic scene, add a background image to one or both of the background plane and the support plane, and generate a pair of images based on the synthetic scene, wherein a first image in the pair of images is a depth image of the synthetic scene, and wherein a second image in the pair of images is a color image of the synthetic scene.