Surrogate Image Generation for Landmark Detection
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Solution Overview
Problem
Current image processing techniques for annotating landmarks on real two-dimensional images face challenges in accurately estimating object poses and generating reliable training datasets, especially when dealing with diverse real-world variations and large datasets required for machine learning models.
Innovation Solution
The method involves generating a training set of images by estimating object poses in real images, creating surrogate images using a three-dimensional model, perturbing characteristics to mimic real-world variations, and iteratively refining landmark annotations and pose estimations, ultimately improving the accuracy of landmark detection and pose estimation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real images are used directly for training landmark detection models, then the training process is simple, but the accuracy of pose estimation and landmark detection is insufficient due to lack of diverse real-world variations
Solution Approach 1:
The patent creates synthetic surrogate images by rendering 3D models of objects, copying their geometric and textural properties. These synthetic images serve as training data substitutes, providing diverse poses and variations without requiring extensive manual annotation of real images, thereby improving pose estimation accuracy while managing data generation complexity
Solution Approach 2:
The system varies multiple parameters in the 3D model rendering process including lighting conditions, camera angles, object poses, and environmental factors. By systematically changing these parameters, the method generates diverse training images that capture real-world variations, improving model generalization and estimation accuracy
2Reliability
If large datasets are collected for machine learning training, then model robustness improves, but the time and resources required for data collection and annotation increase significantly
Solution Approach 1:
The system automatically generates its own training data by rendering 3D models with known ground truth annotations. This self-service approach eliminates the need for manual image collection and annotation, as the synthetic data generation process inherently provides labeled training examples, significantly reducing time investment while building robust models
Solution Approach 2:
The method performs preliminary rendering of 3D models to create synthetic training images before the actual model training begins. By pre-generating diverse training data with known annotations, the system prepares comprehensive datasets in advance, avoiding time-consuming data collection during the training phase and enabling faster model development
3Quantity of substance
If pose estimation is performed on all real images, then comprehensive training data is obtained, but computational resources and processing time are excessively consumed
Solution Approach 1:
Instead of estimating poses from 2D images and hoping for accurate results, the patent inverts the approach by rendering 3D models with known poses to create synthetic images. This inversion provides ground truth pose information directly, eliminating the need for computationally intensive pose estimation on all real images while still generating comprehensive training data with accurate annotations
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image processing that involves annotating landmarks on real two-dimensional images. In one aspect, the methods include generating a training set of images of an object for landmark detection. This includes receiving a collection of real images of an object, estimating a pose of the object in each real image in a proper subset of the collection of real images, creating a collection of surrogate images of the object for the training set using the estimated poses and a three-dimensional model of the object.