Machine Learning Key Point Detection with Synthetic View Variations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models struggle to accurately identify key points in images, especially when there is relative movement between the camera and the scene, leading to challenges in creating a three-dimensional representation from multiple two-dimensional images.
Innovation Solution
A method involving synthetically generated image contents is used to transform training images into variations, maintaining scene perspective while adding objects from different perspectives, which are then processed by a trainable machine learning model to optimize key point recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional transformations are used to generate training variations, then the training process is simple and automated, but the model cannot adequately handle relative movement between camera and scene
Solution Approach 1:
The patent uses synthetic image generation to create virtual training data that replicates real-world imaging conditions. Synthetic images are generated from 3D models and rendered from multiple camera positions, creating realistic variations that mimic actual photography conditions including motion blur, perspective changes, and occlusions. This allows the model to learn robust key point detection without requiring complex manual annotation or transformation processes.
Solution Approach 2:
The patent systematically varies multiple parameters when generating synthetic training images, including camera position, angle, focal length, lighting conditions, and object poses. By changing these parameters across the training dataset, the model learns to identify key points that remain consistent across different imaging conditions, thereby improving reliability while maintaining automated training through programmatic parameter variation.
2Measurement precision
If manually annotated training data is used, then key point identification accuracy can be improved, but the manual effort and time required increase significantly
Solution Approach 1:
The synthetic image generation system automatically creates training data without human intervention. 3D models are rendered from multiple virtual camera positions, and key points are automatically extracted from the ground truth 3D structures. This self-service approach eliminates manual annotation while maintaining high precision through the inherent geometric accuracy of the 3D models and rendering pipeline.
Solution Approach 2:
Instead of manually creating training data, the system copies and transforms 3D model representations into 2D synthetic images. The 3D models provide ground truth annotations that are automatically projected onto image planes, creating training pairs where the ground truth key points are precisely known. This copying approach from 3D to 2D domain eliminates manual labeling while preserving measurement precision.
3Adaptability or versatility
If only real images are used for training, then the model learns from actual imaging conditions, but the model lacks robustness to perspective changes and occlusions
Solution Approach 1:
The patent transitions from 2D real images to 3D models for training data generation. By working in the 3D domain, the system can systematically vary camera positions, angles, and perspectives to create training images that cover the entire parameter space of possible viewpoints. This dimensional transition enables comprehensive coverage of perspective changes and occlusion scenarios that would be difficult to achieve with 2D images alone.
Solution Approach 2:
The system performs preliminary generation of synthetic training images before the model training begins. By pre-generating a diverse set of synthetic images covering various perspectives, lighting conditions, and occlusion scenarios, the model is exposed to these edge cases during training. This preliminary action ensures the model learns robust features before being deployed to handle real-world variability.
Data Source
AI summary
A method for training a machine learning model which is configured to identify easily recognizable key points in an input image. The method includes: providing a set of training images; transforming each training image into a variation which contains contents of the training image at other positions; adding synthetically generated image contents to each training image and to its variation, which show the same semantic contents from different perspectives; ascertaining key points for the training image on the one hand and for the variation on the other, using the machine learning model; evaluating using a given cost function the extent to which corresponding key points of the training image and its variation relate to corresponding image contents; and optimizing parameters characterizing the behavior of the machine learning model, with the aim of improving the evaluation by the cost function during further processing of training images and variations.