Compact Object Image Synthesis for Markerless Pose Estimation
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Solution Overview
Problem
Conventional methods for programming augmented reality devices to recognize and estimate the pose of complex objects require significant expertise, effort, and large numbers of image examples, making it challenging to implement effective augmented reality applications efficiently.
Innovation Solution
A method using compact object image data, comprising a small number of unique images, to generate a training target dataset by overlaying manipulated object depictions onto diverse background images, enabling the construction of a robust machine learning model for pose estimation without relying on recognizable markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to train machine learning models for pose estimation, then model accuracy can be improved, but the time and resources required for data collection and model construction increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating synthetic training images and preparing training data before the actual model training process. The synthetic image generation creates diverse training samples in advance, eliminating the need for time-consuming manual data collection and preparation, thus resolving the contradiction between accuracy and construction time
Solution Approach 2:
The system uses copying by generating synthetic images that replicate real-world scenarios through computer graphics. These synthetic copies serve as training data, replacing the need to collect extensive real images manually, thereby maintaining training quality while dramatically reducing data collection time and effort
2Reliability
If extensive manual programming and training is performed, then model robustness can be improved, but the complexity and expertise required increase significantly
Solution Approach 1:
The system implements self-service by enabling automatic model construction with minimal human intervention. The system autonomously generates synthetic training data, configures training parameters, and trains the model without requiring extensive manual programming or expert knowledge, thus maintaining robustness while reducing construction complexity
Solution Approach 2:
The system replaces manual mechanical processes (expert-driven data collection, labeling, and model tuning) with automated computational processes. Machine learning algorithms automatically generate synthetic images and train models, substituting human expertise with algorithmic automation, thereby reducing complexity while maintaining reliability
3Quantity of substance
If large numbers of image examples are collected, then training data quality can be improved, but the effort and resources required for data collection increase significantly
Solution Approach 1:
The system uses copying by generating synthetic images through computer graphics that replicate diverse real-world scenarios. This creates large quantities of training data automatically without manual collection efforts, resolving the contradiction between data quantity and collection effort by replacing physical data gathering with digital synthesis
Solution Approach 2:
The system performs preliminary action by pre-generating diverse synthetic training images covering various poses, lighting conditions, and backgrounds before model training. This preliminary data preparation ensures sufficient training data quantity is available immediately, eliminating the need for time-consuming manual data collection while maintaining data diversity and quality
Data Source
AI summary
An illustrative model construction system may access object image data representative of one or more images depicting an object having a plurality of labeled keypoint features. Based on the object image dataset, the model construction system may generate a training target dataset including a plurality of training target images. Each training target image may be generated by selecting a background image distinct from the object image data, manipulating a depiction of the object represented within the object image data, and overlaying the manipulated depiction of the object onto the selected background image with the labeled keypoint features. Based on this training target dataset, the model construction system may train a machine learning model to recognize and estimate a pose of the object when the object is depicted in input images analyzed using the trained machine learning model. Corresponding methods and systems are also disclosed.