3D Topology Descriptor Training for Calibration-Robust Object Recognition
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Solution Overview
Problem
Existing machine learning models for robotic object recognition are heavily dependent on the quality of training data and sensor calibration, leading to inefficiencies and lack of explainability in recognizing object topology from images.
Innovation Solution
A method involving the generation of training data image pairs using 3D models, where descriptor component values are adapted to reduce the impact of calibration errors by making interior object descriptors closer to background descriptors near the periphery, enabling robust training and improved edge detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If self-monitored learning is used to train the machine learning model, then the model can learn object descriptors autonomously, but the descriptors lack explainability and are heavily dependent on training data quality
Solution Approach 1:
The patent introduces 3D model vertex positions as an intermediary structure that bridges the gap between image data and descriptor generation. By mapping image pixels to 3D model vertices and using vertex positions as mediators, the system creates explainable descriptors that trace back to geometric properties rather than opaque neural network transformations
Solution Approach 2:
The patent transforms the descriptor generation process by changing from direct pixel-based learning to vertex position-based descriptors. By using 3D model vertex coordinates and transforming them through camera pose parameters, the system creates descriptors with clear geometric meaning that are explainable and robust to variations
2Measurement precision
If sharp edges are used in target images for training, then object boundaries are clearly defined, but the training becomes sensitive to calibration errors and position mismatches
Solution Approach 1:
The patent applies beforehand cushioning by intentionally blurring the edges in target images before training. This pre-processing step creates a buffer zone that absorbs the impact of calibration errors and position mismatches, preventing them from causing catastrophic failures during inference
Solution Approach 2:
The patent applies different quality levels to different regions of the image: interior regions maintain clear definitions while boundary regions are intentionally blurred. This local differentiation allows the model to learn precise object interiors while being robust to boundary detection errors
3Measurement precision
If high-quality training data with precise calibration is used, then the model achieves high accuracy, but the system becomes complex and difficult to implement
Solution Approach 1:
The patent uses 3D model copies as synthetic training data instead of requiring complex real-world captured images. By generating target images from 3D models with known vertex positions and camera poses, the system obtains high-quality training data without the complexity of precise physical calibration
Solution Approach 2:
The system generates its own training data autonomously by using 3D models and simulated camera views. This self-service approach eliminates the need for external calibration equipment and manual data collection, reducing implementation complexity while maintaining high data quality
Data Source
AI summary
A method for training a machine learning model to recognize an object topology of an object from an image of the object. The method includes: obtaining a 3D model of the object; determining a descriptor component value for each vertex of the grid; generating training data image pairs each having a training input image and a target image. The target image is generated by determining the vertex positions in the training input image; assigning the descriptor component value determined for the vertex at the vertex position to the position in the target image; and adapting at least some of the descriptor component values assigned to the positions in the target image or adding descriptor component values to the positions of the target image.