Synthetic Image Generation for Object Detection Training Data
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Solution Overview
Problem
Collecting real images of objects from a broad range of views at high angular resolution is time-consuming and challenging, especially for difficult angles, making it hard to capture sufficient images for effective object detection and tracking in computer vision applications.
Innovation Solution
A method that involves specifying a 3D model, setting camera parameters, and generating training data using both real and synthetic images to enable object detection and tracking, allowing for the use of fewer real images and generating filler images for missing view angles, thereby simplifying the image collection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real images are collected from a broad range of views at high angular resolution, then object detection accuracy is improved, but the time and effort required for image collection increases significantly
Solution Approach 1:
The system performs preliminary actions by collecting a limited set of real images at key viewpoints and pre-computing synthetic images for surrounding angles using 3D models. This preparation phase creates a hybrid training dataset that combines real image quality with comprehensive angular coverage, eliminating the need for exhaustive real image collection while maintaining detection accuracy.
Solution Approach 2:
The system creates copies by generating synthetic images from 3D models to supplement real images. These synthetic copies fill in missing angular perspectives by rendering virtual views that mimic real camera perspectives, providing comprehensive training data without requiring physical capture of every angle.
2Adaptability or versatility
If real images are collected from difficult-to-reach angles, then comprehensive view coverage is achieved, but the complexity of the collection process increases
Solution Approach 1:
The system introduces an intermediary approach by using 3D models as mediators between real images and synthetic renderings. The 3D models serve as intermediate representations that can be rendered from any angle, bridging the gap between limited real image coverage and comprehensive view requirements without requiring complex physical image collection procedures.
Solution Approach 2:
The system performs preliminary 3D model creation and synthetic image generation to cover difficult-to-reach angles before actual object detection is needed. This advance preparation creates a complete training dataset including perspectives that would be challenging to capture in real-time, simplifying the overall collection process.
3Quantity of substance
If dozens of pictures are captured throughout a hemispherical range, then sufficient training data is obtained, but the user burden and time consumption increase
Solution Approach 1:
The system applies partial action by collecting real images only at essential viewpoints rather than attempting to capture every possible angle. Synthetic images are then generated to provide the excessive coverage needed for comprehensive training, allowing the system to achieve sufficient training data quantity with minimal real image collection effort.
Solution Approach 2:
The system achieves universality by creating a hybrid training dataset that serves multiple functions: real images provide authentic texture and lighting information, while synthetic images provide comprehensive angular coverage. This multi-functional dataset approach eliminates the need for exhaustive real image collection while maintaining sufficient training data quantity.
Data Source
AI summary
A method of detecting an object in a real scene using a computer includes specifying a 3D model corresponding to the object. The method further includes acquiring, from a capture camera, an image frame of a reference object captured from a first view angle. The method further includes generating a 2D synthetic image by rendering the 3D model in a second view angle that is different from the first view angle. The method further includes generating training data using (i) the image frame, (ii) the 2D synthetic image, (iii) the run-time camera parameter, and (iv) the capture camera parameter. The method further includes storing the generated training data in one or more memories.